packages <- c("agricolae", "dplyr", "plyr", "ggplot2", "readr", "ggpubr", "car",
"rcompanion", "tidyverse", "ggsignif", "reshape")
installed_packages <- packages %in% rownames(installed.packages())
if (any(installed_packages == FALSE)) {
install.packages(packages[!installed_packages])
}
invisible(lapply(packages, library, character.only = TRUE))
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
## ------------------------------------------------------------------------------
## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)
## ------------------------------------------------------------------------------
##
## Attaching package: 'plyr'
## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## Attaching package: 'ggpubr'
## The following object is masked from 'package:plyr':
##
## mutate
## Loading required package: carData
##
## Attaching package: 'car'
## The following object is masked from 'package:dplyr':
##
## recode
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ tibble 3.1.4 ✓ stringr 1.4.0
## ✓ tidyr 1.1.3 ✓ forcats 0.5.1
## ✓ purrr 0.3.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x plyr::arrange() masks dplyr::arrange()
## x purrr::compact() masks plyr::compact()
## x plyr::count() masks dplyr::count()
## x plyr::failwith() masks dplyr::failwith()
## x dplyr::filter() masks stats::filter()
## x plyr::id() masks dplyr::id()
## x dplyr::lag() masks stats::lag()
## x ggpubr::mutate() masks plyr::mutate(), dplyr::mutate()
## x car::recode() masks dplyr::recode()
## x plyr::rename() masks dplyr::rename()
## x purrr::some() masks car::some()
## x plyr::summarise() masks dplyr::summarise()
## x plyr::summarize() masks dplyr::summarize()
##
## Attaching package: 'reshape'
## The following objects are masked from 'package:tidyr':
##
## expand, smiths
## The following objects are masked from 'package:plyr':
##
## rename, round_any
## The following object is masked from 'package:dplyr':
##
## rename
In this case, we assume you have cloned/donwloaded this repository to your “Documents” folder.
Change directory on mac/linux:
setwd(“/Users/YOURUSERNAME/Documents/X.necrophora.secondaryMetabolites/output”)
Change directory on Windows (Windows 10 in this example):
setwd(“C:/Users/YOURUSERNAME/Documents/X.necrophora.secondaryMetabolites/output”)
pdf(file = "/Users/YOURUSERNAME/Documents/X.necrophora.secondaryMetabolites/output/ Figure1.pdf", # The directory you want to save the file in
width = 7, # The width of the plot in inches
height = 5) # The height of the plot in inches
dev.off()
setwd("/Users/tedggarcia/Documents/X.necrophora.secondaryMetabolites/output/")
ES2 = First experiment for 14 Days of exporuse (DOE)
#ES4 = Repetetion for 14 DOE
ES5 = First experiment for 7 DOE
#ES8 = Repetition for 7 DOE
#ES13A = Experiment testing potentially resistant cultivars (7DOE)
ES13B = Repetition of ES13A
ES14A = Experiment testing effects among plant species (7DOE)
#ES14B = Repetition of ES14A
ES2 <- read.csv("../raw_data/ES2.ChlorophyllContent.14DOE.Exp1.csv", header = T)
ES5 <- read.csv("../raw_data/ES5.ChlorophyllContent.7DOE.Exp1.csv", header = T)
ES13B <- read.csv("../raw_data/ES13B.ChlorophyllContent.7DOE.Exp2.Cultivars.csv",
header = T)
ES14A <- read.csv("../raw_data/ES14A.ChlorophyllContent.7DOE.Exp1.PlantSpecies.csv",
header = T)
shapiro.test(ES2$chl)
##
## Shapiro-Wilk normality test
##
## data: ES2$chl
## W = 0.74674, p-value < 2.2e-16
shapiro.test(ES5$chl)
##
## Shapiro-Wilk normality test
##
## data: ES5$chl
## W = 0.95514, p-value = 5.341e-10
shapiro.test(ES13B$chl)
##
## Shapiro-Wilk normality test
##
## data: ES13B$chl
## W = 0.95496, p-value = 2.7e-07
shapiro.test(ES14A$chl)
##
## Shapiro-Wilk normality test
##
## data: ES14A$chl
## W = 0.95203, p-value = 1.513e-06
ggdensity(ES2$chl, main = "Density of Chlorophyll Content (digital) for ES2",
xlab = "Datapoints")
## Warning: Removed 60 rows containing non-finite values (stat_density).
ggdensity(ES5$chl, main = "Density of Chlorophyll Content (digital) for ES5",
xlab = "Datapoints")
## Warning: Removed 12 rows containing non-finite values (stat_density).
ggdensity(ES13B$chl, main = "Density of Chlorophyll Content (digital) for ES13B",
xlab = "Datapoints")
## Warning: Removed 6 rows containing non-finite values (stat_density).
ggdensity(ES14A$chl, main = "Density of Chlorophyll Content (digital) for ES14A",
xlab = "Datapoints")
## Warning: Removed 3 rows containing non-finite values (stat_density).
ggqqplot(ES2$chl)
## Warning: Removed 60 rows containing non-finite values (stat_qq).
## Warning: Removed 60 rows containing non-finite values (stat_qq_line).
## Warning: Removed 60 rows containing non-finite values (stat_qq_line).
ggqqplot(ES5$chl)
## Warning: Removed 12 rows containing non-finite values (stat_qq).
## Warning: Removed 12 rows containing non-finite values (stat_qq_line).
## Warning: Removed 12 rows containing non-finite values (stat_qq_line).
ggqqplot(ES13B$chl)
## Warning: Removed 6 rows containing non-finite values (stat_qq).
## Warning: Removed 6 rows containing non-finite values (stat_qq_line).
## Warning: Removed 6 rows containing non-finite values (stat_qq_line).
ggqqplot(ES14A$chl)
## Warning: Removed 3 rows containing non-finite values (stat_qq).
## Warning: Removed 3 rows containing non-finite values (stat_qq_line).
## Warning: Removed 3 rows containing non-finite values (stat_qq_line).
plotNormalHistogram(ES2$chl, main = "Density of Chlorophyll Content (Digital) for ES2",
xlab = "Datapoints")
plotNormalHistogram(ES5$chl, main = "Density of Chlorophyll Content (Digital) for ES5",
xlab = "Datapoints")
plotNormalHistogram(ES13B$chl, main = "Density of Chlorophyll Content (Digital) for E13B",
xlab = "Datapoints")
plotNormalHistogram(ES14A$chl, main = "Density of Chlorophyll Content (Digital) for E14A",
xlab = "Datapoints")
ES2_chl.tuk = transformTukey(ES2$chl, plotit=FALSE)
##
## lambda W Shapiro.p.value
## 416 0.375 0.9449 3.664e-09
##
## if (lambda > 0){TRANS = x ^ lambda}
## if (lambda == 0){TRANS = log(x)}
## if (lambda < 0){TRANS = -1 * x ^ lambda}
ES5_chl.tuk = transformTukey(ES5$chl, plotit=FALSE)
##
## lambda W Shapiro.p.value
## 427 0.65 0.9695 1.098e-07
##
## if (lambda > 0){TRANS = x ^ lambda}
## if (lambda == 0){TRANS = log(x)}
## if (lambda < 0){TRANS = -1 * x ^ lambda}
ES13B_chl.tuk = transformTukey(ES13B$chl, plotit=FALSE)
##
## lambda W Shapiro.p.value
## 432 0.775 0.9604 1.226e-06
##
## if (lambda > 0){TRANS = x ^ lambda}
## if (lambda == 0){TRANS = log(x)}
## if (lambda < 0){TRANS = -1 * x ^ lambda}
ES14A_chl.tuk = transformTukey(ES14A$chl, plotit=FALSE)
##
## lambda W Shapiro.p.value
## 470 1.725 0.979 0.00282
##
## if (lambda > 0){TRANS = x ^ lambda}
## if (lambda == 0){TRANS = log(x)}
## if (lambda < 0){TRANS = -1 * x ^ lambda}
ES2.mod <- cbind(ES2, ES2_chl.tuk)
ES5.mod <- cbind(ES5, ES5_chl.tuk)
ES13B.mod <- cbind(ES13B, ES13B_chl.tuk)
ES14A.mod <- cbind(ES14A, ES14A_chl.tuk)
Run ANOVA and Tukey’s honest significance differences for raw chlorophyll content.
As desribed above, this experiment was ran using cell-free culture filtrates (CFCFs) from three local strains of Xylaria necrophora (DMCC2126, DMCC2127, and DMCC2165) and one Colletotrichum siamense (DMCC2966) for 14 days (ES2)
###############ES2 analysis (raw data)################################
ES2.chl.anova <- lm (ES2$chl ~ ES2$Treatment +
ES2$Dilution +
ES2$Condition +
ES2$isoRep +
ES2$techRep +
ES2$sampleNumber)
ES2.chl.anova
##
## Call:
## lm(formula = ES2$chl ~ ES2$Treatment + ES2$Dilution + ES2$Condition +
## ES2$isoRep + ES2$techRep + ES2$sampleNumber)
##
## Coefficients:
## (Intercept) ES2$TreatmentDMCC2126 ES2$TreatmentDMCC2127
## 236.806 -140.175 -173.159
## ES2$TreatmentDMCC2165 ES2$TreatmentDMCC2966 ES2$Dilution25fold
## -169.865 -44.126 -102.848
## ES2$ConditionStationary ES2$isoRepisolateRep2 ES2$techRepStem2
## -8.823 23.729 -24.695
## ES2$techRepStem3 ES2$sampleNumbersample2 ES2$sampleNumbersample3
## 16.950 26.386 30.435
summary(ES2.chl.anova)
##
## Call:
## lm(formula = ES2$chl ~ ES2$Treatment + ES2$Dilution + ES2$Condition +
## ES2$isoRep + ES2$techRep + ES2$sampleNumber)
##
## Residuals:
## Min 1Q Median 3Q Max
## -231.24 -49.47 1.55 41.40 536.42
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 236.806 20.365 11.628 < 2e-16 ***
## ES2$TreatmentDMCC2126 -140.175 18.956 -7.395 1.70e-12 ***
## ES2$TreatmentDMCC2127 -173.159 19.204 -9.017 < 2e-16 ***
## ES2$TreatmentDMCC2165 -169.865 18.952 -8.963 < 2e-16 ***
## ES2$TreatmentDMCC2966 -44.126 18.481 -2.388 0.0176 *
## ES2$Dilution25fold -102.848 11.998 -8.572 7.35e-16 ***
## ES2$ConditionStationary -8.823 11.944 -0.739 0.4607
## ES2$isoRepisolateRep2 23.729 11.964 1.983 0.0483 *
## ES2$techRepStem2 -24.695 15.316 -1.612 0.1080
## ES2$techRepStem3 16.950 14.020 1.209 0.2277
## ES2$sampleNumbersample2 26.386 14.436 1.828 0.0687 .
## ES2$sampleNumbersample3 30.435 14.489 2.101 0.0366 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 100.5 on 276 degrees of freedom
## (72 observations deleted due to missingness)
## Multiple R-squared: 0.4591, Adjusted R-squared: 0.4375
## F-statistic: 21.3 on 11 and 276 DF, p-value: < 2.2e-16
anova(ES2.chl.anova)
## Analysis of Variance Table
##
## Response: ES2$chl
## Df Sum Sq Mean Sq F value Pr(>F)
## ES2$Treatment 4 1458908 364727 36.1018 < 2.2e-16 ***
## ES2$Dilution 1 732380 732380 72.4932 1.094e-15 ***
## ES2$Condition 1 3246 3246 0.3213 0.57128
## ES2$isoRep 1 38119 38119 3.7732 0.05310 .
## ES2$techRep 2 80731 40366 3.9955 0.01947 *
## ES2$sampleNumber 2 53280 26640 2.6369 0.07338 .
## Residuals 276 2788355 10103
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Tukey's HSD for Variable chl by Treament
ES2.chl.treatment.HSD.test <- HSD.test(ES2.chl.anova, 'ES2$Treatment', group = T)
ES2.chl.treatment.HSD.test
## $statistics
## MSerror Df Mean CV
## 10102.73 276 105.3393 95.41771
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES2$Treatment 5 3.883285 0.05
##
## $means
## ES2$chl std r Min Max Q25 Q50 Q75
## control 206.91423 217.07353 57 0 831.472 26.54900 138.046 272.67000
## DMCC2126 73.25279 74.61783 57 0 281.899 11.31300 29.554 129.60000
## DMCC2127 37.91085 49.89550 54 0 167.994 8.52575 15.327 49.05425
## DMCC2165 30.48823 45.19861 57 0 187.945 8.36200 14.000 20.43000
## DMCC2966 167.98710 89.73008 63 0 309.266 119.20850 177.714 233.30650
##
## $comparison
## NULL
##
## $groups
## ES2$chl groups
## control 206.91423 a
## DMCC2966 167.98710 a
## DMCC2126 73.25279 b
## DMCC2127 37.91085 b
## DMCC2165 30.48823 b
##
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable chl by Dilution
ES2.chl.dilution.HSD.test <- HSD.test(ES2.chl.anova, 'ES2$Dilution', group = T)
ES2.chl.dilution.HSD.test
## $statistics
## MSerror Df Mean CV
## 10102.73 276 105.3393 95.41771
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES2$Dilution 2 2.784016 0.05
##
## $means
## ES2$chl std r Min Max Q25 Q50 Q75
## 100fold 157.13270 159.97363 138 0 831.472 36.10000 129.1440 206.71875
## 25fold 57.68939 79.35162 150 0 309.266 9.85425 15.6685 99.75575
##
## $comparison
## NULL
##
## $groups
## ES2$chl groups
## 100fold 157.13270 a
## 25fold 57.68939 b
##
## attr(,"class")
## [1] "group"
#Complete ANOVA for ES2 by treatment by dilution
ES2.comp.HSD.group <- HSD.test(ES2.chl.anova, c("ES2$Treatment", "ES2$Dilution"), group=TRUE,console=TRUE)
##
## Study: ES2.chl.anova ~ c("ES2$Treatment", "ES2$Dilution")
##
## HSD Test for ES2$chl
##
## Mean Square Error: 10102.73
##
## ES2$Treatment:ES2$Dilution, means
##
## ES2.chl std r Min Max
## control:100fold 383.864000 223.675014 24 97.748 831.472
## control:25fold 78.223485 77.070835 33 0.000 268.776
## DMCC2126:100fold 127.480933 64.977439 30 10.433 281.899
## DMCC2126:25fold 12.999296 10.944223 27 0.000 51.676
## DMCC2127:100fold 58.980593 59.597226 27 0.000 167.994
## DMCC2127:25fold 16.841111 24.515869 27 0.000 112.319
## DMCC2165:100fold 58.801375 58.889805 24 0.000 187.945
## DMCC2165:25fold 9.896848 6.632284 33 0.000 19.414
## DMCC2966:100fold 171.013333 97.165275 33 0.000 301.867
## DMCC2966:25fold 164.658233 82.303611 30 0.000 309.266
##
## Alpha: 0.05 ; DF Error: 276
## Critical Value of Studentized Range: 4.511094
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## ES2$chl groups
## control:100fold 383.864000 a
## DMCC2966:100fold 171.013333 b
## DMCC2966:25fold 164.658233 b
## DMCC2126:100fold 127.480933 bc
## control:25fold 78.223485 cd
## DMCC2127:100fold 58.980593 cd
## DMCC2165:100fold 58.801375 cd
## DMCC2127:25fold 16.841111 d
## DMCC2126:25fold 12.999296 d
## DMCC2165:25fold 9.896848 d
ES2.comp.HSD.group
## $statistics
## MSerror Df Mean CV
## 10102.73 276 105.3393 95.41771
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES2$Treatment:ES2$Dilution 10 4.511094 0.05
##
## $means
## ES2$chl std r Min Max Q25 Q50
## control:100fold 383.864000 223.675014 24 97.748 831.472 244.69000 280.5385
## control:25fold 78.223485 77.070835 33 0.000 268.776 15.68300 59.4900
## DMCC2126:100fold 127.480933 64.977439 30 10.433 281.899 81.64425 129.1440
## DMCC2126:25fold 12.999296 10.944223 27 0.000 51.676 9.86550 11.3130
## DMCC2127:100fold 58.980593 59.597226 27 0.000 167.994 12.11000 35.6240
## DMCC2127:25fold 16.841111 24.515869 27 0.000 112.319 0.00000 11.9040
## DMCC2165:100fold 58.801375 58.889805 24 0.000 187.945 14.21225 25.3885
## DMCC2165:25fold 9.896848 6.632284 33 0.000 19.414 0.00000 12.2830
## DMCC2966:100fold 171.013333 97.165275 33 0.000 301.867 118.40500 176.8540
## DMCC2966:25fold 164.658233 82.303611 30 0.000 309.266 120.78250 181.5795
## Q75
## control:100fold 527.0058
## control:25fold 129.7670
## DMCC2126:100fold 159.8775
## DMCC2126:25fold 16.5335
## DMCC2127:100fold 90.5650
## DMCC2127:25fold 15.6860
## DMCC2165:100fold 105.9032
## DMCC2165:25fold 14.7740
## DMCC2966:100fold 241.9460
## DMCC2966:25fold 222.5877
##
## $comparison
## NULL
##
## $groups
## ES2$chl groups
## control:100fold 383.864000 a
## DMCC2966:100fold 171.013333 b
## DMCC2966:25fold 164.658233 b
## DMCC2126:100fold 127.480933 bc
## control:25fold 78.223485 cd
## DMCC2127:100fold 58.980593 cd
## DMCC2165:100fold 58.801375 cd
## DMCC2127:25fold 16.841111 d
## DMCC2126:25fold 12.999296 d
## DMCC2165:25fold 9.896848 d
##
## attr(,"class")
## [1] "group"
###############ES2 analysis (normalized dataset) ################################
ES2.mod.chl.anova <- lm (ES2.mod$ES2_chl.tuk ~ ES2.mod$Treatment +
ES2.mod$Dilution +
ES2.mod$Condition +
ES2.mod$isoRep +
ES2.mod$techRep +
ES2.mod$sampleNumber)
ES2.mod.chl.anova
##
## Call:
## lm(formula = ES2.mod$ES2_chl.tuk ~ ES2.mod$Treatment + ES2.mod$Dilution +
## ES2.mod$Condition + ES2.mod$isoRep + ES2.mod$techRep + ES2.mod$sampleNumber)
##
## Coefficients:
## (Intercept) ES2.mod$TreatmentDMCC2126
## 7.52662 -2.19660
## ES2.mod$TreatmentDMCC2127 ES2.mod$TreatmentDMCC2165
## -3.39025 -3.45003
## ES2.mod$TreatmentDMCC2966 ES2.mod$Dilution25fold
## -0.21011 -2.34945
## ES2.mod$ConditionStationary ES2.mod$isoRepisolateRep2
## -0.09975 0.73788
## ES2.mod$techRepStem2 ES2.mod$techRepStem3
## -0.70265 -0.27113
## ES2.mod$sampleNumbersample2 ES2.mod$sampleNumbersample3
## -0.03389 -0.09430
summary(ES2.mod.chl.anova)
##
## Call:
## lm(formula = ES2.mod$ES2_chl.tuk ~ ES2.mod$Treatment + ES2.mod$Dilution +
## ES2.mod$Condition + ES2.mod$isoRep + ES2.mod$techRep + ES2.mod$sampleNumber)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.1829 -1.1889 0.4416 1.2936 4.5838
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.52662 0.44329 16.979 < 2e-16 ***
## ES2.mod$TreatmentDMCC2126 -2.19660 0.41262 -5.323 2.11e-07 ***
## ES2.mod$TreatmentDMCC2127 -3.39025 0.41803 -8.110 1.67e-14 ***
## ES2.mod$TreatmentDMCC2165 -3.45003 0.41254 -8.363 3.06e-15 ***
## ES2.mod$TreatmentDMCC2966 -0.21011 0.40229 -0.522 0.60190
## ES2.mod$Dilution25fold -2.34945 0.26117 -8.996 < 2e-16 ***
## ES2.mod$ConditionStationary -0.09975 0.26000 -0.384 0.70152
## ES2.mod$isoRepisolateRep2 0.73788 0.26043 2.833 0.00495 **
## ES2.mod$techRepStem2 -0.70265 0.33340 -2.108 0.03597 *
## ES2.mod$techRepStem3 -0.27113 0.30518 -0.888 0.37510
## ES2.mod$sampleNumbersample2 -0.03389 0.31425 -0.108 0.91420
## ES2.mod$sampleNumbersample3 -0.09430 0.31539 -0.299 0.76518
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.188 on 276 degrees of freedom
## (72 observations deleted due to missingness)
## Multiple R-squared: 0.4559, Adjusted R-squared: 0.4342
## F-statistic: 21.02 on 11 and 276 DF, p-value: < 2.2e-16
anova(ES2.mod.chl.anova)
## Analysis of Variance Table
##
## Response: ES2.mod$ES2_chl.tuk
## Df Sum Sq Mean Sq F value Pr(>F)
## ES2.mod$Treatment 4 680.08 170.02 35.5165 < 2.2e-16 ***
## ES2.mod$Dilution 1 367.55 367.55 76.7802 < 2.2e-16 ***
## ES2.mod$Condition 1 0.63 0.63 0.1326 0.716072
## ES2.mod$isoRep 1 36.95 36.95 7.7190 0.005839 **
## ES2.mod$techRep 2 21.22 10.61 2.2166 0.110912
## ES2.mod$sampleNumber 2 0.44 0.22 0.0456 0.955457
## Residuals 276 1321.23 4.79
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Tukey's HSD for Variable chl (tukey trans) by Treament
ES2.mod.chl.treatment.HSD.test <- HSD.test(ES2.mod.chl.anova, 'ES2.mod$Treatment', group = T)
ES2.mod.chl.treatment.HSD.test
## $statistics
## MSerror Df Mean CV
## 4.787063 276 4.479861 48.83937
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES2.mod$Treatment 5 3.883285 0.05
##
## $means
## ES2.mod$ES2_chl.tuk std r Min Max Q25 Q50
## control 6.207956 3.276161 57 0 12.443509 3.419937 6.346130
## DMCC2126 4.140619 2.307227 57 0 8.294402 2.483657 3.560255
## DMCC2127 2.929858 2.131941 54 0 6.831014 2.232076 2.783162
## DMCC2165 2.663168 1.976045 57 0 7.124617 2.217514 2.690283
## DMCC2966 6.195529 2.505798 63 0 8.587655 6.006381 6.976629
## Q75
## control 8.191511
## DMCC2126 6.197648
## DMCC2127 4.305207
## DMCC2165 3.099921
## DMCC2966 7.725989
##
## $comparison
## NULL
##
## $groups
## ES2.mod$ES2_chl.tuk groups
## control 6.207956 a
## DMCC2966 6.195529 a
## DMCC2126 4.140619 b
## DMCC2127 2.929858 c
## DMCC2165 2.663168 c
##
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable chl (tukey trans) by Dilution
ES2.mod.chl.dilution.HSD.test <- HSD.test(ES2.mod.chl.anova, 'ES2.mod$Dilution', group = T)
ES2.mod.chl.dilution.HSD.test
## $statistics
## MSerror Df Mean CV
## 4.787063 276 4.479861 48.83937
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES2.mod$Dilution 2 2.784016 0.05
##
## $means
## ES2.mod$ES2_chl.tuk std r Min Max Q25 Q50
## 100fold 5.670079 2.877306 138 0 12.443509 3.837417 6.189452
## 25fold 3.384861 2.482893 150 0 8.587655 2.358352 2.806307
## Q75
## 100fold 7.383524
## 25fold 5.616963
##
## $comparison
## NULL
##
## $groups
## ES2.mod$ES2_chl.tuk groups
## 100fold 5.670079 a
## 25fold 3.384861 b
##
## attr(,"class")
## [1] "group"
#Complete ANOVA for ES2.mod by treatment by dilution (tukey trans)
ES2.mod.comp.HSD.group <- HSD.test(ES2.mod.chl.anova, c("ES2.mod$Treatment",
"ES2.mod$Dilution"),
group=TRUE,console=TRUE)
##
## Study: ES2.mod.chl.anova ~ c("ES2.mod$Treatment", "ES2.mod$Dilution")
##
## HSD Test for ES2.mod$ES2_chl.tuk
##
## Mean Square Error: 4.787063
##
## ES2.mod$Treatment:ES2.mod$Dilution, means
##
## ES2.mod.ES2_chl.tuk std r Min Max
## control:100fold 8.952842 2.033695 24 5.575585 12.443509
## control:25fold 4.211675 2.459674 33 0.000000 8.147445
## DMCC2126:100fold 5.904452 1.432971 30 2.409370 8.294402
## DMCC2126:25fold 2.180805 1.263683 27 0.000000 4.390190
## DMCC2127:100fold 3.720246 2.309541 27 0.000000 6.831014
## DMCC2127:25fold 2.139470 1.622868 27 0.000000 5.873811
## DMCC2165:100fold 3.677465 2.368645 24 0.000000 7.124617
## DMCC2165:25fold 1.925497 1.211620 33 0.000000 3.041187
## DMCC2966:100fold 6.114039 2.778697 33 0.000000 8.510026
## DMCC2966:25fold 6.285168 2.210961 30 0.000000 8.587655
##
## Alpha: 0.05 ; DF Error: 276
## Critical Value of Studentized Range: 4.511094
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## ES2.mod$ES2_chl.tuk groups
## control:100fold 8.952842 a
## DMCC2966:25fold 6.285168 b
## DMCC2966:100fold 6.114039 b
## DMCC2126:100fold 5.904452 bc
## control:25fold 4.211675 cd
## DMCC2127:100fold 3.720246 de
## DMCC2165:100fold 3.677465 de
## DMCC2126:25fold 2.180805 e
## DMCC2127:25fold 2.139470 e
## DMCC2165:25fold 1.925497 e
ES2.mod.comp.HSD.group
## $statistics
## MSerror Df Mean CV
## 4.787063 276 4.479861 48.83937
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES2.mod$Treatment:ES2.mod$Dilution 10 4.511094 0.05
##
## $means
## ES2.mod$ES2_chl.tuk std r Min Max Q25
## control:100fold 8.952842 2.033695 24 5.575585 12.443509 7.860042
## control:25fold 4.211675 2.459674 33 0.000000 8.147445 2.807281
## DMCC2126:100fold 5.904452 1.432971 30 2.409370 8.294402 5.211560
## DMCC2126:25fold 2.180805 1.263683 27 0.000000 4.390190 2.359361
## DMCC2127:100fold 3.720246 2.309541 27 0.000000 6.831014 2.547399
## DMCC2127:25fold 2.139470 1.622868 27 0.000000 5.873811 0.000000
## DMCC2165:100fold 3.677465 2.368645 24 0.000000 7.124617 2.700544
## DMCC2165:25fold 1.925497 1.211620 33 0.000000 3.041187 0.000000
## DMCC2966:100fold 6.114039 2.778697 33 0.000000 8.510026 5.991199
## DMCC2966:25fold 6.285168 2.210961 30 0.000000 8.587655 6.035946
## Q50 Q75
## control:100fold 8.279323 10.486003
## control:25fold 4.628247 6.200641
## DMCC2126:100fold 6.189452 6.705312
## DMCC2126:25fold 2.483657 2.863395
## DMCC2127:100fold 3.818594 5.417472
## DMCC2127:25fold 2.531540 2.807481
## DMCC2165:100fold 3.362478 5.745663
## DMCC2165:25fold 2.561469 2.745123
## DMCC2966:100fold 6.963949 7.832392
## DMCC2966:25fold 7.032779 7.590879
##
## $comparison
## NULL
##
## $groups
## ES2.mod$ES2_chl.tuk groups
## control:100fold 8.952842 a
## DMCC2966:25fold 6.285168 b
## DMCC2966:100fold 6.114039 b
## DMCC2126:100fold 5.904452 bc
## control:25fold 4.211675 cd
## DMCC2127:100fold 3.720246 de
## DMCC2165:100fold 3.677465 de
## DMCC2126:25fold 2.180805 e
## DMCC2127:25fold 2.139470 e
## DMCC2165:25fold 1.925497 e
##
## attr(,"class")
## [1] "group"
This test was run for 7 DOE and photos were taken of the last day of exposure.
###############ES5 analysis################################
ES5.chl.anova <- lm (ES5$chl ~ ES5$Treatment +
ES5$Dilution +
ES5$Condition +
ES5$isoRep +
ES5$techRep +
ES5$sampleNumber)
ES5.chl.anova
##
## Call:
## lm(formula = ES5$chl ~ ES5$Treatment + ES5$Dilution + ES5$Condition +
## ES5$isoRep + ES5$techRep + ES5$sampleNumber)
##
## Coefficients:
## (Intercept) ES5$TreatmentDMCC2126 ES5$TreatmentDMCC2127
## 192.365 -61.618 -70.990
## ES5$TreatmentDMCC2165 ES5$Dilution25fold ES5$ConditionStationary
## -67.429 -46.539 42.178
## ES5$isoRepisolateRep2 ES5$isoRepisolateRep3 ES5$techRepstemRep2
## -9.981 -22.792 -14.269
## ES5$techRepstemRep3 ES5$sampleNumbersample2 ES5$sampleNumbersample3
## 19.985 11.399 25.312
summary(ES5.chl.anova)
##
## Call:
## lm(formula = ES5$chl ~ ES5$Treatment + ES5$Dilution + ES5$Condition +
## ES5$isoRep + ES5$techRep + ES5$sampleNumber)
##
## Residuals:
## Min 1Q Median 3Q Max
## -182.445 -40.817 -5.474 42.676 187.396
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 192.365 10.836 17.753 < 2e-16 ***
## ES5$TreatmentDMCC2126 -61.618 8.801 -7.001 1.05e-11 ***
## ES5$TreatmentDMCC2127 -70.990 8.734 -8.128 5.27e-15 ***
## ES5$TreatmentDMCC2165 -67.429 8.798 -7.664 1.33e-13 ***
## ES5$Dilution25fold -46.539 6.177 -7.534 3.19e-13 ***
## ES5$ConditionStationary 42.178 6.177 6.828 3.13e-11 ***
## ES5$isoRepisolateRep2 -9.981 7.580 -1.317 0.188662
## ES5$isoRepisolateRep3 -22.792 7.534 -3.025 0.002642 **
## ES5$techRepstemRep2 -14.269 7.620 -1.873 0.061849 .
## ES5$techRepstemRep3 19.985 7.536 2.652 0.008315 **
## ES5$sampleNumbersample2 11.399 7.557 1.509 0.132197
## ES5$sampleNumbersample3 25.312 7.557 3.350 0.000884 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 63.22 on 408 degrees of freedom
## (12 observations deleted due to missingness)
## Multiple R-squared: 0.3665, Adjusted R-squared: 0.3494
## F-statistic: 21.46 on 11 and 408 DF, p-value: < 2.2e-16
anova(ES5.chl.anova)
## Analysis of Variance Table
##
## Response: ES5$chl
## Df Sum Sq Mean Sq F value Pr(>F)
## ES5$Treatment 3 351053 117018 29.2750 < 2.2e-16 ***
## ES5$Dilution 1 239796 239796 59.9912 7.615e-14 ***
## ES5$Condition 1 186231 186231 46.5904 3.179e-11 ***
## ES5$isoRep 2 37850 18925 4.7345 0.009275 **
## ES5$techRep 2 83616 41808 10.4593 3.717e-05 ***
## ES5$sampleNumber 2 44997 22498 5.6285 0.003879 **
## Residuals 408 1630853 3997
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Tukey's HSD for Variable chl by Treament
ES5.chl.treatment.HSD.test <- HSD.test(ES5.chl.anova, 'ES5$Treatment', group = T)
ES5.chl.treatment.HSD.test
## $statistics
## MSerror Df Mean CV
## 3997.188 408 143.1371 44.16975
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES5$Treatment 4 3.648176 0.05
##
## $means
## ES5$chl std r Min Max Q25 Q50 Q75
## control 193.8353 69.20948 102 26.0 372.6 147.750 202.9 240.55
## DMCC2126 131.8714 73.80466 105 30.3 277.2 63.700 110.8 189.60
## DMCC2127 122.4120 75.41655 108 0.0 339.2 64.875 100.3 157.95
## DMCC2165 126.4705 73.67261 105 0.0 289.2 68.300 100.0 189.20
##
## $comparison
## NULL
##
## $groups
## ES5$chl groups
## control 193.8353 a
## DMCC2126 131.8714 b
## DMCC2165 126.4705 b
## DMCC2127 122.4120 b
##
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable chl by Dilution
ES5.chl.dilution.HSD.test <- HSD.test(ES5.chl.anova, 'ES5$Dilution', group = T)
ES5.chl.dilution.HSD.test
## $statistics
## MSerror Df Mean CV MSD
## 3997.188 408 143.1371 44.16975 12.12889
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES5$Dilution 2 2.780054 0.05
##
## $means
## ES5$chl std r Min Max Q25 Q50 Q75
## 100fold 166.9881 77.60533 210 0 372.6 99.475 178.20 232.825
## 25fold 119.2862 71.77681 210 0 303.7 61.000 94.45 174.500
##
## $comparison
## NULL
##
## $groups
## ES5$chl groups
## 100fold 166.9881 a
## 25fold 119.2862 b
##
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable chl by Condition
ES5.chl.cond.HSD.test <- HSD.test(ES5.chl.anova, 'ES5$Condition', group = T)
ES5.chl.cond.HSD.test
## $statistics
## MSerror Df Mean CV MSD
## 3997.188 408 143.1371 44.16975 12.12889
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES5$Condition 2 2.780054 0.05
##
## $means
## ES5$chl std r Min Max Q25 Q50 Q75
## Shaking 121.3619 70.21004 210 0 363.9 63.900 99.15 174.075
## Stationary 164.9124 80.22074 210 0 372.6 91.075 179.40 234.325
##
## $comparison
## NULL
##
## $groups
## ES5$chl groups
## Stationary 164.9124 a
## Shaking 121.3619 b
##
## attr(,"class")
## [1] "group"
#Complete ANOVA for ES5 by treatment by condition, by dilution
ES5.comp.HSD.group <- HSD.test(ES5.chl.anova, c("ES5$Treatment", "ES5$Condition",
"ES5$Dilution"), group=TRUE,console=TRUE)
##
## Study: ES5.chl.anova ~ c("ES5$Treatment", "ES5$Condition", "ES5$Dilution")
##
## HSD Test for ES5$chl
##
## Mean Square Error: 3997.188
##
## ES5$Treatment:ES5$Condition:ES5$Dilution, means
##
## ES5.chl std r Min Max
## control:Shaking:100fold 200.02083 68.81458 24 104.0 363.9
## control:Shaking:25fold 158.22593 62.18883 27 26.0 249.0
## control:Stationary:100fold 238.50000 37.84527 27 185.5 372.6
## control:Stationary:25fold 177.46250 78.47053 24 37.7 303.7
## DMCC2126:Shaking:100fold 161.77500 70.00547 24 48.0 270.4
## DMCC2126:Shaking:25fold 75.53333 30.56325 27 30.3 140.8
## DMCC2126:Stationary:100fold 174.24815 63.63720 27 51.7 264.4
## DMCC2126:Stationary:25fold 119.25185 79.48387 27 36.0 277.2
## DMCC2127:Shaking:100fold 93.23333 39.13111 27 37.1 190.5
## DMCC2127:Shaking:25fold 61.84444 32.99067 27 0.0 119.5
## DMCC2127:Stationary:100fold 192.10370 77.79170 27 75.0 339.2
## DMCC2127:Stationary:25fold 142.46667 67.68053 27 53.1 296.0
## DMCC2165:Shaking:100fold 143.79630 71.66806 27 36.8 273.2
## DMCC2165:Shaking:25fold 89.69630 40.84195 27 40.3 174.5
## DMCC2165:Stationary:100fold 135.31852 85.93666 27 0.0 279.9
## DMCC2165:Stationary:25fold 138.39583 79.51052 24 48.0 289.2
##
## Alpha: 0.05 ; DF Error: 408
## Critical Value of Studentized Range: 4.87582
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## ES5$chl groups
## control:Stationary:100fold 238.50000 a
## control:Shaking:100fold 200.02083 ab
## DMCC2127:Stationary:100fold 192.10370 abc
## control:Stationary:25fold 177.46250 abcd
## DMCC2126:Stationary:100fold 174.24815 bcd
## DMCC2126:Shaking:100fold 161.77500 bcd
## control:Shaking:25fold 158.22593 bcd
## DMCC2165:Shaking:100fold 143.79630 bcde
## DMCC2127:Stationary:25fold 142.46667 bcde
## DMCC2165:Stationary:25fold 138.39583 bcde
## DMCC2165:Stationary:100fold 135.31852 cde
## DMCC2126:Stationary:25fold 119.25185 def
## DMCC2127:Shaking:100fold 93.23333 ef
## DMCC2165:Shaking:25fold 89.69630 ef
## DMCC2126:Shaking:25fold 75.53333 f
## DMCC2127:Shaking:25fold 61.84444 f
ES5.comp.HSD.group
## $statistics
## MSerror Df Mean CV
## 3997.188 408 143.1371 44.16975
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES5$Treatment:ES5$Condition:ES5$Dilution 16 4.87582 0.05
##
## $means
## ES5$chl std r Min Max Q25 Q50
## control:Shaking:100fold 200.02083 68.81458 24 104.0 363.9 146.800 186.90
## control:Shaking:25fold 158.22593 62.18883 27 26.0 249.0 126.400 174.80
## control:Stationary:100fold 238.50000 37.84527 27 185.5 372.6 215.850 235.10
## control:Stationary:25fold 177.46250 78.47053 24 37.7 303.7 124.350 193.05
## DMCC2126:Shaking:100fold 161.77500 70.00547 24 48.0 270.4 87.125 173.00
## DMCC2126:Shaking:25fold 75.53333 30.56325 27 30.3 140.8 50.050 66.50
## DMCC2126:Stationary:100fold 174.24815 63.63720 27 51.7 264.4 132.600 180.00
## DMCC2126:Stationary:25fold 119.25185 79.48387 27 36.0 277.2 56.400 85.00
## DMCC2127:Shaking:100fold 93.23333 39.13111 27 37.1 190.5 69.800 85.80
## DMCC2127:Shaking:25fold 61.84444 32.99067 27 0.0 119.5 45.400 58.90
## DMCC2127:Stationary:100fold 192.10370 77.79170 27 75.0 339.2 109.700 204.20
## DMCC2127:Stationary:25fold 142.46667 67.68053 27 53.1 296.0 78.050 131.20
## DMCC2165:Shaking:100fold 143.79630 71.66806 27 36.8 273.2 78.650 113.60
## DMCC2165:Shaking:25fold 89.69630 40.84195 27 40.3 174.5 60.100 77.90
## DMCC2165:Stationary:100fold 135.31852 85.93666 27 0.0 279.9 61.950 158.00
## DMCC2165:Stationary:25fold 138.39583 79.51052 24 48.0 289.2 73.150 114.70
## Q75
## control:Shaking:100fold 245.325
## control:Shaking:25fold 205.700
## control:Stationary:100fold 253.200
## control:Stationary:25fold 238.750
## DMCC2126:Shaking:100fold 207.875
## DMCC2126:Shaking:25fold 94.400
## DMCC2126:Stationary:100fold 230.800
## DMCC2126:Stationary:25fold 173.250
## DMCC2127:Shaking:100fold 116.550
## DMCC2127:Shaking:25fold 88.250
## DMCC2127:Stationary:100fold 249.300
## DMCC2127:Stationary:25fold 186.700
## DMCC2165:Shaking:100fold 201.750
## DMCC2165:Shaking:25fold 94.600
## DMCC2165:Stationary:100fold 205.300
## DMCC2165:Stationary:25fold 191.025
##
## $comparison
## NULL
##
## $groups
## ES5$chl groups
## control:Stationary:100fold 238.50000 a
## control:Shaking:100fold 200.02083 ab
## DMCC2127:Stationary:100fold 192.10370 abc
## control:Stationary:25fold 177.46250 abcd
## DMCC2126:Stationary:100fold 174.24815 bcd
## DMCC2126:Shaking:100fold 161.77500 bcd
## control:Shaking:25fold 158.22593 bcd
## DMCC2165:Shaking:100fold 143.79630 bcde
## DMCC2127:Stationary:25fold 142.46667 bcde
## DMCC2165:Stationary:25fold 138.39583 bcde
## DMCC2165:Stationary:100fold 135.31852 cde
## DMCC2126:Stationary:25fold 119.25185 def
## DMCC2127:Shaking:100fold 93.23333 ef
## DMCC2165:Shaking:25fold 89.69630 ef
## DMCC2126:Shaking:25fold 75.53333 f
## DMCC2127:Shaking:25fold 61.84444 f
##
## attr(,"class")
## [1] "group"
###############ES5 analysis (normalized data) ################################
ES5.mod.chl.anova <- lm (ES5.mod$ES5_chl.tuk ~ ES5.mod$Treatment +
ES5.mod$Dilution +
ES5.mod$Condition +
ES5.mod$isoRep +
ES5.mod$techRep +
ES5.mod$sampleNumber)
ES5.mod.chl.anova
##
## Call:
## lm(formula = ES5.mod$ES5_chl.tuk ~ ES5.mod$Treatment + ES5.mod$Dilution +
## ES5.mod$Condition + ES5.mod$isoRep + ES5.mod$techRep + ES5.mod$sampleNumber)
##
## Coefficients:
## (Intercept) ES5.mod$TreatmentDMCC2126
## 30.278 -7.067
## ES5.mod$TreatmentDMCC2127 ES5.mod$TreatmentDMCC2165
## -8.357 -7.928
## ES5.mod$Dilution25fold ES5.mod$ConditionStationary
## -5.443 4.789
## ES5.mod$isoRepisolateRep2 ES5.mod$isoRepisolateRep3
## -1.403 -2.930
## ES5.mod$techRepstemRep2 ES5.mod$techRepstemRep3
## -1.392 2.517
## ES5.mod$sampleNumbersample2 ES5.mod$sampleNumbersample3
## 1.050 2.548
summary(ES5.mod.chl.anova)
##
## Call:
## lm(formula = ES5.mod$ES5_chl.tuk ~ ES5.mod$Treatment + ES5.mod$Dilution +
## ES5.mod$Condition + ES5.mod$isoRep + ES5.mod$techRep + ES5.mod$sampleNumber)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.2842 -4.6883 -0.0798 5.3904 19.2000
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 30.2777 1.3012 23.270 < 2e-16 ***
## ES5.mod$TreatmentDMCC2126 -7.0672 1.0568 -6.687 7.50e-11 ***
## ES5.mod$TreatmentDMCC2127 -8.3567 1.0488 -7.968 1.63e-14 ***
## ES5.mod$TreatmentDMCC2165 -7.9283 1.0565 -7.505 3.90e-13 ***
## ES5.mod$Dilution25fold -5.4428 0.7417 -7.338 1.18e-12 ***
## ES5.mod$ConditionStationary 4.7890 0.7417 6.457 3.05e-10 ***
## ES5.mod$isoRepisolateRep2 -1.4026 0.9102 -1.541 0.12411
## ES5.mod$isoRepisolateRep3 -2.9300 0.9047 -3.239 0.00130 **
## ES5.mod$techRepstemRep2 -1.3920 0.9150 -1.521 0.12896
## ES5.mod$techRepstemRep3 2.5171 0.9049 2.782 0.00566 **
## ES5.mod$sampleNumbersample2 1.0500 0.9074 1.157 0.24789
## ES5.mod$sampleNumbersample3 2.5484 0.9074 2.808 0.00522 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.592 on 408 degrees of freedom
## (12 observations deleted due to missingness)
## Multiple R-squared: 0.3506, Adjusted R-squared: 0.3331
## F-statistic: 20.02 on 11 and 408 DF, p-value: < 2.2e-16
anova(ES5.mod.chl.anova)
## Analysis of Variance Table
##
## Response: ES5.mod$ES5_chl.tuk
## Df Sum Sq Mean Sq F value Pr(>F)
## ES5.mod$Treatment 3 4830.6 1610.2 27.9375 < 2.2e-16 ***
## ES5.mod$Dilution 1 3271.4 3271.4 56.7598 3.204e-13 ***
## ES5.mod$Condition 1 2403.9 2403.9 41.7082 3.018e-10 ***
## ES5.mod$isoRep 2 618.1 309.1 5.3623 0.005027 **
## ES5.mod$techRep 2 1110.4 555.2 9.6327 8.172e-05 ***
## ES5.mod$sampleNumber 2 459.3 229.6 3.9845 0.019330 *
## Residuals 408 23515.2 57.6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Tukey's HSD for Variable chl (tukey trans) by Treament
ES5.mod.chl.treatment.HSD.test <- HSD.test(ES5.mod.chl.anova, 'ES5.mod$Treatment',
group = T)
ES5.mod.chl.treatment.HSD.test
## $statistics
## MSerror Df Mean CV
## 57.63529 408 24.21363 31.35338
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES5.mod$Treatment 4 3.648176 0.05
##
## $means
## ES5.mod$ES5_chl.tuk std r Min Max Q25 Q50
## control 30.14562 7.609997 102 8.312519 46.91458 25.71521 31.60326
## DMCC2126 23.01374 8.709822 105 9.182009 38.70932 14.88301 21.32803
## DMCC2127 21.72436 9.135639 108 0.000000 44.13634 15.06086 19.99151
## DMCC2165 22.21139 9.151154 105 0.000000 39.79045 15.57304 19.95262
## Q75
## control 35.30039
## DMCC2126 30.24091
## DMCC2127 26.85501
## DMCC2165 30.19943
##
## $comparison
## NULL
##
## $groups
## ES5.mod$ES5_chl.tuk groups
## control 30.14562 a
## DMCC2126 23.01374 b
## DMCC2165 22.21139 b
## DMCC2127 21.72436 b
##
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable chl (tukey trans) by Dilution
ES5.mod.chl.dilution.HSD.test <- HSD.test(ES5.mod.chl.anova, 'ES5.mod$Dilution',
group = T)
ES5.mod.chl.dilution.HSD.test
## $statistics
## MSerror Df Mean CV MSD
## 57.63529 408 24.21363 31.35338 1.456424
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES5.mod$Dilution 2 2.780054 0.05
##
## $means
## ES5.mod$ES5_chl.tuk std r Min Max Q25 Q50
## 100fold 26.99820 9.029696 210 0 46.91458 19.88445 29.04621
## 25fold 21.42906 8.725273 210 0 41.07609 14.46985 19.22561
## Q75
## 100fold 34.55964
## 25fold 28.65280
##
## $comparison
## NULL
##
## $groups
## ES5.mod$ES5_chl.tuk groups
## 100fold 26.99820 a
## 25fold 21.42906 b
##
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable chl (tukey trans) by Condition
ES5.mod.chl.cond.HSD.test <- HSD.test(ES5.mod.chl.anova, 'ES5.mod$Condition', group = T)
ES5.mod.chl.cond.HSD.test
## $statistics
## MSerror Df Mean CV MSD
## 57.63529 408 24.21363 31.35338 1.456424
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES5.mod$Condition 2 2.780054 0.05
##
## $means
## ES5.mod$ES5_chl.tuk std r Min Max Q25 Q50
## Shaking 21.73611 8.509070 210 0 46.19961 14.91331 19.84222
## Stationary 26.69114 9.407897 210 0 46.91458 18.77627 29.17320
## Q75
## Shaking 28.60736
## Stationary 34.70420
##
## $comparison
## NULL
##
## $groups
## ES5.mod$ES5_chl.tuk groups
## Stationary 26.69114 a
## Shaking 21.73611 b
##
## attr(,"class")
## [1] "group"
#Complete ANOVA for ES5.mod by treatment by condition, by dilution (tukey trans)
ES5.mod.comp.HSD.group <- HSD.test(ES5.mod.chl.anova, c("ES5.mod$Treatment",
"ES5.mod$Condition",
"ES5.mod$Dilution"),
group=TRUE,console=TRUE)
##
## Study: ES5.mod.chl.anova ~ c("ES5.mod$Treatment", "ES5.mod$Condition", "ES5.mod$Dilution")
##
## HSD Test for ES5.mod$ES5_chl.tuk
##
## Mean Square Error: 57.63529
##
## ES5.mod$Treatment:ES5.mod$Condition:ES5.mod$Dilution, means
##
## ES5.mod.ES5_chl.tuk std r Min Max
## control:Shaking:100fold 30.92160 6.877550 24 20.467824 46.19961
## control:Shaking:25fold 26.28822 7.584437 27 8.312519 36.10186
## control:Stationary:100fold 35.01510 3.511991 27 29.814226 46.91458
## control:Stationary:25fold 28.23105 8.847762 24 10.583319 41.07609
## DMCC2126:Shaking:100fold 26.63321 8.061394 24 12.382456 38.08942
## DMCC2126:Shaking:25fold 16.33068 4.334703 27 9.182009 24.92251
## DMCC2126:Stationary:100fold 28.13045 7.218510 27 12.994778 37.53790
## DMCC2126:Stationary:25fold 21.36280 9.304178 27 10.270619 38.70932
## DMCC2127:Shaking:100fold 18.70574 5.119409 27 10.473529 30.33414
## DMCC2127:Shaking:25fold 13.80400 6.101857 27 0.000000 22.40212
## DMCC2127:Stationary:100fold 29.90084 8.268740 27 16.549688 44.13634
## DMCC2127:Stationary:25fold 24.48688 7.726440 27 13.222435 40.39612
## DMCC2165:Shaking:100fold 24.56023 8.205647 27 10.418401 38.34533
## DMCC2165:Shaking:25fold 18.20998 5.258465 27 11.052188 28.65280
## DMCC2165:Stationary:100fold 22.51380 11.836979 27 0.000000 38.95398
## DMCC2165:Stationary:25fold 23.73032 9.225861 24 12.382456 39.79045
##
## Alpha: 0.05 ; DF Error: 408
## Critical Value of Studentized Range: 4.87582
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## ES5.mod$ES5_chl.tuk groups
## control:Stationary:100fold 35.01510 a
## control:Shaking:100fold 30.92160 ab
## DMCC2127:Stationary:100fold 29.90084 ab
## control:Stationary:25fold 28.23105 abc
## DMCC2126:Stationary:100fold 28.13045 abc
## DMCC2126:Shaking:100fold 26.63321 bc
## control:Shaking:25fold 26.28822 bc
## DMCC2165:Shaking:100fold 24.56023 bcd
## DMCC2127:Stationary:25fold 24.48688 bcd
## DMCC2165:Stationary:25fold 23.73032 bcd
## DMCC2165:Stationary:100fold 22.51380 cde
## DMCC2126:Stationary:25fold 21.36280 cde
## DMCC2127:Shaking:100fold 18.70574 def
## DMCC2165:Shaking:25fold 18.20998 def
## DMCC2126:Shaking:25fold 16.33068 ef
## DMCC2127:Shaking:25fold 13.80400 f
ES5.mod.comp.HSD.group
## $statistics
## MSerror Df Mean CV
## 57.63529 408 24.21363 31.35338
##
## $parameters
## test name.t ntr
## Tukey ES5.mod$Treatment:ES5.mod$Condition:ES5.mod$Dilution 16
## StudentizedRange alpha
## 4.87582 0.05
##
## $means
## ES5.mod$ES5_chl.tuk std r Min Max
## control:Shaking:100fold 30.92160 6.877550 24 20.467824 46.19961
## control:Shaking:25fold 26.28822 7.584437 27 8.312519 36.10186
## control:Stationary:100fold 35.01510 3.511991 27 29.814226 46.91458
## control:Stationary:25fold 28.23105 8.847762 24 10.583319 41.07609
## DMCC2126:Shaking:100fold 26.63321 8.061394 24 12.382456 38.08942
## DMCC2126:Shaking:25fold 16.33068 4.334703 27 9.182009 24.92251
## DMCC2126:Stationary:100fold 28.13045 7.218510 27 12.994778 37.53790
## DMCC2126:Stationary:25fold 21.36280 9.304178 27 10.270619 38.70932
## DMCC2127:Shaking:100fold 18.70574 5.119409 27 10.473529 30.33414
## DMCC2127:Shaking:25fold 13.80400 6.101857 27 0.000000 22.40212
## DMCC2127:Stationary:100fold 29.90084 8.268740 27 16.549688 44.13634
## DMCC2127:Stationary:25fold 24.48688 7.726440 27 13.222435 40.39612
## DMCC2165:Shaking:100fold 24.56023 8.205647 27 10.418401 38.34533
## DMCC2165:Shaking:25fold 18.20998 5.258465 27 11.052188 28.65280
## DMCC2165:Stationary:100fold 22.51380 11.836979 27 0.000000 38.95398
## DMCC2165:Stationary:25fold 23.73032 9.225861 24 12.382456 39.79045
## Q25 Q50 Q75
## control:Shaking:100fold 25.60774 29.95578 35.75395
## control:Shaking:25fold 23.23414 28.68481 31.88435
## control:Stationary:100fold 32.89985 34.77877 36.49626
## control:Stationary:25fold 22.97996 30.57655 35.12646
## DMCC2126:Shaking:100fold 18.24222 28.49017 32.10254
## DMCC2126:Shaking:25fold 12.72170 15.30503 19.21892
## DMCC2126:Stationary:100fold 23.96670 29.23662 34.36364
## DMCC2126:Stationary:25fold 13.75020 17.95239 28.49754
## DMCC2127:Shaking:100fold 15.79238 18.06204 22.03937
## DMCC2127:Shaking:25fold 11.94225 14.14409 18.38373
## DMCC2127:Stationary:100fold 21.18901 31.73483 36.13001
## DMCC2127:Stationary:25fold 16.98374 23.80439 29.93923
## DMCC2165:Shaking:100fold 17.06819 21.67684 31.48681
## DMCC2165:Shaking:25fold 14.32943 16.96287 19.24541
## DMCC2165:Stationary:100fold 14.61527 26.86130 31.84574
## DMCC2165:Stationary:25fold 16.28243 21.81134 30.38458
##
## $comparison
## NULL
##
## $groups
## ES5.mod$ES5_chl.tuk groups
## control:Stationary:100fold 35.01510 a
## control:Shaking:100fold 30.92160 ab
## DMCC2127:Stationary:100fold 29.90084 ab
## control:Stationary:25fold 28.23105 abc
## DMCC2126:Stationary:100fold 28.13045 abc
## DMCC2126:Shaking:100fold 26.63321 bc
## control:Shaking:25fold 26.28822 bc
## DMCC2165:Shaking:100fold 24.56023 bcd
## DMCC2127:Stationary:25fold 24.48688 bcd
## DMCC2165:Stationary:25fold 23.73032 bcd
## DMCC2165:Stationary:100fold 22.51380 cde
## DMCC2126:Stationary:25fold 21.36280 cde
## DMCC2127:Shaking:100fold 18.70574 def
## DMCC2165:Shaking:25fold 18.20998 def
## DMCC2126:Shaking:25fold 16.33068 ef
## DMCC2127:Shaking:25fold 13.80400 f
##
## attr(,"class")
## [1] "group"
Testing variation among potentially resistant cultivars compared to known susceptible cultivars treated with CFCFs from X. necrophora (isolate DMCC 2165) to determine if resistance to direct application of SMs exist.
#Statistical analysis
#####ES13B###
ES13B.chl.anova <- lm (ES13B$chl ~ ES13B$Treatment +
ES13B$HostVariety +
ES13B$isoRepNumber +
ES13B$techRepNumber +
ES13B$SampleNumber)
ES13B.chl.anova
##
## Call:
## lm(formula = ES13B$chl ~ ES13B$Treatment + ES13B$HostVariety +
## ES13B$isoRepNumber + ES13B$techRepNumber + ES13B$SampleNumber)
##
## Coefficients:
## (Intercept) ES13B$TreatmentDMCC2165
## 187.9400 -105.4678
## ES13B$HostVarietyDG47E80 ES13B$HostVarietyDG47X95
## 27.8736 26.3892
## ES13B$HostVarietyOsage ES13B$HostVarietyP5414LLS
## 16.1981 -3.8273
## ES13B$isoRepNumberisoRep2 ES13B$isoRepNumberisoRep3
## -7.3121 1.4292
## ES13B$techRepNumbertechRep2 ES13B$techRepNumbertechRep3
## 29.6658 8.0253
## ES13B$SampleNumbersample2 ES13B$SampleNumbersample3
## 0.7302 1.9473
summary(ES13B.chl.anova)
##
## Call:
## lm(formula = ES13B$chl ~ ES13B$Treatment + ES13B$HostVariety +
## ES13B$isoRepNumber + ES13B$techRepNumber + ES13B$SampleNumber)
##
## Residuals:
## Min 1Q Median 3Q Max
## -219.035 -47.751 -4.823 42.506 237.651
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 187.9400 17.1352 10.968 <2e-16 ***
## ES13B$TreatmentDMCC2165 -105.4678 9.9569 -10.592 <2e-16 ***
## ES13B$HostVarietyDG47E80 27.8736 15.5401 1.794 0.0741 .
## ES13B$HostVarietyDG47X95 26.3892 16.0431 1.645 0.1012
## ES13B$HostVarietyOsage 16.1981 15.5401 1.042 0.2983
## ES13B$HostVarietyP5414LLS -3.8273 15.5401 -0.246 0.8057
## ES13B$isoRepNumberisoRep2 -7.3121 12.2504 -0.597 0.5511
## ES13B$isoRepNumberisoRep3 1.4292 12.1499 0.118 0.9065
## ES13B$techRepNumbertechRep2 29.6658 12.1499 2.442 0.0153 *
## ES13B$techRepNumbertechRep3 8.0253 12.1499 0.661 0.5095
## ES13B$SampleNumbersample2 0.7302 12.1733 0.060 0.9522
## ES13B$SampleNumbersample3 1.9473 12.1733 0.160 0.8730
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 80.75 on 252 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.337, Adjusted R-squared: 0.308
## F-statistic: 11.64 on 11 and 252 DF, p-value: < 2.2e-16
anova(ES13B.chl.anova)
## Analysis of Variance Table
##
## Response: ES13B$chl
## Df Sum Sq Mean Sq F value Pr(>F)
## ES13B$Treatment 1 745236 745236 114.2939 < 2e-16 ***
## ES13B$HostVariety 4 44757 11189 1.7160 0.14689
## ES13B$isoRepNumber 2 3558 1779 0.2728 0.76144
## ES13B$techRepNumber 2 41380 20690 3.1731 0.04355 *
## ES13B$SampleNumber 2 170 85 0.0131 0.98702
## Residuals 252 1643127 6520
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Tukey's HSD for Variable chl by Treatment
ES13B.chl.treatment.HSD.test <- HSD.test(ES13B.chl.anova, 'ES13B$Treatment', group = T)
ES13B.chl.treatment.HSD.test
## $statistics
## MSerror Df Mean CV
## 6520.345 252 160.8255 50.20887
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES13B$Treatment 2 2.785184 0.05
##
## $means
## ES13B$chl std r Min Max Q25 Q50 Q75
## Control 212.7620 79.79142 135 0 402.241 166.577 220.922 257.822
## DMCC2165 106.4733 82.90892 129 0 350.226 51.563 71.243 161.827
##
## $comparison
## NULL
##
## $groups
## ES13B$chl groups
## Control 212.7620 a
## DMCC2165 106.4733 b
##
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable chl by Soybean Cultivar
ES13B.chl.host_variety.HSD.test <- HSD.test(ES13B.chl.anova, 'ES13B$HostVariety', group = T)
ES13B.chl.host_variety.HSD.test
## $statistics
## MSerror Df Mean CV
## 6520.345 252 160.8255 50.20887
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES13B$HostVariety 5 3.885737 0.05
##
## $means
## ES13B$chl std r Min Max Q25 Q50 Q75
## AG4632 146.7014 89.25074 54 0 364.618 68.0405 134.5050 212.5315
## DG47E80 174.5750 94.89959 54 0 372.762 94.9610 203.4325 233.2080
## DG47X95 179.1090 97.67480 48 0 359.307 86.1180 192.7220 255.9690
## Osage 162.8995 111.37700 54 0 402.241 66.9080 155.3500 248.0178
## P5414LLS 142.8741 88.83067 54 0 318.243 59.8260 153.8800 221.6343
##
## $comparison
## NULL
##
## $groups
## ES13B$chl groups
## DG47X95 179.1090 a
## DG47E80 174.5750 a
## Osage 162.8995 a
## AG4632 146.7014 a
## P5414LLS 142.8741 a
##
## attr(,"class")
## [1] "group"
#Complete ANOVA for ES13B
ES13B.comp.HSD.group <- HSD.test(ES13B.chl.anova, c("ES13B$Treatment", "ES13B$HostVariety"),
group=TRUE,console=TRUE)
##
## Study: ES13B.chl.anova ~ c("ES13B$Treatment", "ES13B$HostVariety")
##
## HSD Test for ES13B$chl
##
## Mean Square Error: 6520.345
##
## ES13B$Treatment:ES13B$HostVariety, means
##
## ES13B.chl std r Min Max
## Control:AG4632 190.99715 86.60398 27 0.000 364.618
## Control:DG47E80 228.60578 74.03698 27 99.638 372.762
## Control:DG47X95 217.34011 75.28029 27 62.560 359.307
## Control:Osage 236.66259 98.93830 27 0.000 402.241
## Control:P5414LLS 190.20437 49.79161 27 96.055 269.571
## DMCC2165:AG4632 102.40559 68.28138 27 0.000 279.119
## DMCC2165:DG47E80 120.54422 82.54428 27 0.000 268.043
## DMCC2165:DG47X95 129.95467 102.67650 21 0.000 350.226
## DMCC2165:Osage 89.13633 64.78778 27 0.000 305.544
## DMCC2165:P5414LLS 95.54374 94.62256 27 0.000 318.243
##
## Alpha: 0.05 ; DF Error: 252
## Critical Value of Studentized Range: 4.514628
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## ES13B$chl groups
## Control:Osage 236.66259 a
## Control:DG47E80 228.60578 a
## Control:DG47X95 217.34011 a
## Control:AG4632 190.99715 ab
## Control:P5414LLS 190.20437 abc
## DMCC2165:DG47X95 129.95467 bcd
## DMCC2165:DG47E80 120.54422 cd
## DMCC2165:AG4632 102.40559 d
## DMCC2165:P5414LLS 95.54374 d
## DMCC2165:Osage 89.13633 d
ES13B.comp.HSD.group
## $statistics
## MSerror Df Mean CV
## 6520.345 252 160.8255 50.20887
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES13B$Treatment:ES13B$HostVariety 10 4.514628 0.05
##
## $means
## ES13B$chl std r Min Max Q25 Q50
## Control:AG4632 190.99715 86.60398 27 0.000 364.618 144.0055 209.592
## Control:DG47E80 228.60578 74.03698 27 99.638 372.762 206.6285 227.869
## Control:DG47X95 217.34011 75.28029 27 62.560 359.307 180.9375 220.770
## Control:Osage 236.66259 98.93830 27 0.000 402.241 220.1595 246.824
## Control:P5414LLS 190.20437 49.79161 27 96.055 269.571 163.7070 193.690
## DMCC2165:AG4632 102.40559 68.28138 27 0.000 279.119 55.0810 79.594
## DMCC2165:DG47E80 120.54422 82.54428 27 0.000 268.043 49.0770 93.402
## DMCC2165:DG47X95 129.95467 102.67650 21 0.000 350.226 47.3850 81.525
## DMCC2165:Osage 89.13633 64.78778 27 0.000 305.544 63.5255 67.114
## DMCC2165:P5414LLS 95.54374 94.62256 27 0.000 318.243 35.1075 57.848
## Q75
## Control:AG4632 234.2065
## Control:DG47E80 277.7645
## Control:DG47X95 275.6780
## Control:Osage 271.3380
## Control:P5414LLS 228.1255
## DMCC2165:AG4632 128.8945
## DMCC2165:DG47E80 201.5630
## DMCC2165:DG47X95 199.8590
## DMCC2165:Osage 81.3670
## DMCC2165:P5414LLS 102.5945
##
## $comparison
## NULL
##
## $groups
## ES13B$chl groups
## Control:Osage 236.66259 a
## Control:DG47E80 228.60578 a
## Control:DG47X95 217.34011 a
## Control:AG4632 190.99715 ab
## Control:P5414LLS 190.20437 abc
## DMCC2165:DG47X95 129.95467 bcd
## DMCC2165:DG47E80 120.54422 cd
## DMCC2165:AG4632 102.40559 d
## DMCC2165:P5414LLS 95.54374 d
## DMCC2165:Osage 89.13633 d
##
## attr(,"class")
## [1] "group"
#Statistical analysis
#####ES13B.mod###
ES13B.mod.chl.anova <- lm (ES13B.mod$ES13B_chl.tuk ~ ES13B.mod$Treatment +
ES13B.mod$HostVariety +
ES13B.mod$isoRepNumber +
ES13B.mod$techRepNumber +
ES13B.mod$SampleNumber)
ES13B.mod.chl.anova
##
## Call:
## lm(formula = ES13B.mod$ES13B_chl.tuk ~ ES13B.mod$Treatment +
## ES13B.mod$HostVariety + ES13B.mod$isoRepNumber + ES13B.mod$techRepNumber +
## ES13B.mod$SampleNumber)
##
## Coefficients:
## (Intercept) ES13B.mod$TreatmentDMCC2165
## 56.4659 -27.1569
## ES13B.mod$HostVarietyDG47E80 ES13B.mod$HostVarietyDG47X95
## 6.8552 6.4268
## ES13B.mod$HostVarietyOsage ES13B.mod$HostVarietyP5414LLS
## 3.2278 -1.2888
## ES13B.mod$isoRepNumberisoRep2 ES13B.mod$isoRepNumberisoRep3
## -1.8503 0.1216
## ES13B.mod$techRepNumbertechRep2 ES13B.mod$techRepNumbertechRep3
## 7.5512 2.1409
## ES13B.mod$SampleNumbersample2 ES13B.mod$SampleNumbersample3
## 0.6429 0.7374
summary(ES13B.mod.chl.anova)
##
## Call:
## lm(formula = ES13B.mod$ES13B_chl.tuk ~ ES13B.mod$Treatment +
## ES13B.mod$HostVariety + ES13B.mod$isoRepNumber + ES13B.mod$techRepNumber +
## ES13B.mod$SampleNumber)
##
## Residuals:
## Min 1Q Median 3Q Max
## -64.139 -11.806 0.251 11.105 58.266
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 56.4659 4.4320 12.740 <2e-16 ***
## ES13B.mod$TreatmentDMCC2165 -27.1569 2.5754 -10.545 <2e-16 ***
## ES13B.mod$HostVarietyDG47E80 6.8552 4.0195 1.705 0.0893 .
## ES13B.mod$HostVarietyDG47X95 6.4268 4.1496 1.549 0.1227
## ES13B.mod$HostVarietyOsage 3.2278 4.0195 0.803 0.4227
## ES13B.mod$HostVarietyP5414LLS -1.2888 4.0195 -0.321 0.7488
## ES13B.mod$isoRepNumberisoRep2 -1.8503 3.1686 -0.584 0.5598
## ES13B.mod$isoRepNumberisoRep3 0.1216 3.1426 0.039 0.9692
## ES13B.mod$techRepNumbertechRep2 7.5512 3.1426 2.403 0.0170 *
## ES13B.mod$techRepNumbertechRep3 2.1409 3.1426 0.681 0.4963
## ES13B.mod$SampleNumbersample2 0.6429 3.1486 0.204 0.8384
## ES13B.mod$SampleNumbersample3 0.7374 3.1486 0.234 0.8150
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.89 on 252 degrees of freedom
## (6 observations deleted due to missingness)
## Multiple R-squared: 0.3339, Adjusted R-squared: 0.3048
## F-statistic: 11.48 on 11 and 252 DF, p-value: < 2.2e-16
anova(ES13B.mod.chl.anova)
## Analysis of Variance Table
##
## Response: ES13B.mod$ES13B_chl.tuk
## Df Sum Sq Mean Sq F value Pr(>F)
## ES13B.mod$Treatment 1 49427 49427 113.3105 < 2e-16 ***
## ES13B.mod$HostVariety 4 2794 698 1.6010 0.17455
## ES13B.mod$isoRepNumber 2 193 97 0.2216 0.80137
## ES13B.mod$techRepNumber 2 2663 1331 3.0519 0.04902 *
## ES13B.mod$SampleNumber 2 28 14 0.0325 0.96804
## Residuals 252 109925 436
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Tukey's HSD for Variable chl by Treatment
ES13B.mod.chl.treatment.HSD.test <- HSD.test(ES13B.mod.chl.anova, 'ES13B.mod$Treatment',
group = T)
ES13B.mod.chl.treatment.HSD.test
## $statistics
## MSerror Df Mean CV
## 436.2119 252 49.24912 42.40824
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES13B.mod$Treatment 2 2.785184 0.05
##
## $means
## ES13B.mod$ES13B_chl.tuk std r Min Max Q25 Q50
## Control 62.62462 19.91280 135 0 104.34627 52.69278 65.58208
## DMCC2165 35.25151 22.09171 129 0 93.72814 21.23548 27.28194
## Q75
## Control 73.92184
## DMCC2165 51.52459
##
## $comparison
## NULL
##
## $groups
## ES13B.mod$ES13B_chl.tuk groups
## Control 62.62462 a
## DMCC2165 35.25151 b
##
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable chl by Soybean Cultivar
ES13B.mod.chl.host_variety.HSD.test <- HSD.test(ES13B.mod.chl.anova,
'ES13B.mod$HostVariety', group = T)
ES13B.mod.chl.host_variety.HSD.test
## $statistics
## MSerror Df Mean CV
## 436.2119 252 49.24912 42.40824
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES13B.mod$HostVariety 5 3.885737 0.05
##
## $means
## ES13B.mod$ES13B_chl.tuk std r Min Max Q25 Q50
## AG4632 46.00199 23.04112 54 0 96.69957 26.32493 44.64489
## DG47E80 52.85715 24.31248 54 0 98.36929 34.08532 61.52054
## DG47X95 53.96776 24.74253 48 0 95.60617 31.59813 58.99393
## Osage 49.22977 28.48843 54 0 104.34627 25.98636 49.51131
## P5414LLS 44.71323 23.92033 54 0 87.02381 23.82148 49.53890
## Q75
## AG4632 63.64317
## DG47E80 68.39115
## DG47X95 73.50975
## Osage 71.73382
## P5414LLS 65.74587
##
## $comparison
## NULL
##
## $groups
## ES13B.mod$ES13B_chl.tuk groups
## DG47X95 53.96776 a
## DG47E80 52.85715 a
## Osage 49.22977 a
## AG4632 46.00199 a
## P5414LLS 44.71323 a
##
## attr(,"class")
## [1] "group"
#Complete ANOVA for ES13B.mod
ES13B.mod.comp.HSD.group <- HSD.test(ES13B.mod.chl.anova, c("ES13B.mod$Treatment",
"ES13B.mod$HostVariety"),
group=TRUE,console=TRUE)
##
## Study: ES13B.mod.chl.anova ~ c("ES13B.mod$Treatment", "ES13B.mod$HostVariety")
##
## HSD Test for ES13B.mod$ES13B_chl.tuk
##
## Mean Square Error: 436.2119
##
## ES13B.mod$Treatment:ES13B.mod$HostVariety, means
##
## ES13B.mod.ES13B_chl.tuk std r Min Max
## Control:AG4632 57.20904 22.03662 27 0.00000 96.69957
## Control:DG47E80 66.70288 17.20548 27 35.38176 98.36929
## Control:DG47X95 64.01404 17.93361 27 24.66777 95.60617
## Control:Osage 67.15363 26.52535 27 0.00000 104.34627
## Control:P5414LLS 58.04351 11.99680 27 34.39165 76.51943
## DMCC2165:AG4632 34.79495 18.32309 27 0.00000 78.61163
## DMCC2165:DG47E80 39.01143 22.59966 27 0.00000 76.18307
## DMCC2165:DG47X95 41.05111 26.62931 21 0.00000 93.72814
## DMCC2165:Osage 31.30592 16.83924 27 0.00000 84.32030
## DMCC2165:P5414LLS 31.38296 25.56143 27 0.00000 87.02381
##
## Alpha: 0.05 ; DF Error: 252
## Critical Value of Studentized Range: 4.514628
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## ES13B.mod$ES13B_chl.tuk groups
## Control:Osage 67.15363 a
## Control:DG47E80 66.70288 a
## Control:DG47X95 64.01404 a
## Control:P5414LLS 58.04351 ab
## Control:AG4632 57.20904 ab
## DMCC2165:DG47X95 41.05111 bc
## DMCC2165:DG47E80 39.01143 c
## DMCC2165:AG4632 34.79495 c
## DMCC2165:P5414LLS 31.38296 c
## DMCC2165:Osage 31.30592 c
ES13B.mod.comp.HSD.group
## $statistics
## MSerror Df Mean CV
## 436.2119 252 49.24912 42.40824
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES13B.mod$Treatment:ES13B.mod$HostVariety 10 4.514628 0.05
##
## $means
## ES13B.mod$ES13B_chl.tuk std r Min Max
## Control:AG4632 57.20904 22.03662 27 0.00000 96.69957
## Control:DG47E80 66.70288 17.20548 27 35.38176 98.36929
## Control:DG47X95 64.01404 17.93361 27 24.66777 95.60617
## Control:Osage 67.15363 26.52535 27 0.00000 104.34627
## Control:P5414LLS 58.04351 11.99680 27 34.39165 76.51943
## DMCC2165:AG4632 34.79495 18.32309 27 0.00000 78.61163
## DMCC2165:DG47E80 39.01143 22.59966 27 0.00000 76.18307
## DMCC2165:DG47X95 41.05111 26.62931 21 0.00000 93.72814
## DMCC2165:Osage 31.30592 16.83924 27 0.00000 84.32030
## DMCC2165:P5414LLS 31.38296 25.56143 27 0.00000 87.02381
## Q25 Q50 Q75
## Control:AG4632 47.05388 62.96010 68.61805
## Control:DG47E80 62.26497 67.17475 78.30087
## Control:DG47X95 56.17982 65.54711 77.85944
## Control:Osage 65.40652 71.46612 76.90405
## Control:P5414LLS 51.98765 59.22541 67.22892
## DMCC2165:AG4632 22.34657 29.72913 43.18888
## DMCC2165:DG47E80 20.43572 33.65317 61.08024
## DMCC2165:DG47X95 19.88940 30.28659 60.68214
## DMCC2165:Osage 24.95960 26.04835 30.23822
## DMCC2165:P5414LLS 15.75931 23.21526 36.18682
##
## $comparison
## NULL
##
## $groups
## ES13B.mod$ES13B_chl.tuk groups
## Control:Osage 67.15363 a
## Control:DG47E80 66.70288 a
## Control:DG47X95 64.01404 a
## Control:P5414LLS 58.04351 ab
## Control:AG4632 57.20904 ab
## DMCC2165:DG47X95 41.05111 bc
## DMCC2165:DG47E80 39.01143 c
## DMCC2165:AG4632 34.79495 c
## DMCC2165:P5414LLS 31.38296 c
## DMCC2165:Osage 31.30592 c
##
## attr(,"class")
## [1] "group"
This dataset contains chlorophyll content measured among plant species treated with CFCFs from X. necrophora (isolate DMCC 2165) to estimate the specificy of SMs.
#####ES14A###
ES14A.chl.anova <- lm (ES14A$chl ~ ES14A$Treatment +
ES14A$Host + ES14A$isoRepNumber +
ES14A$techRepNumber +
ES14A$LeafSampleNumber)
ES14A.chl.anova
##
## Call:
## lm(formula = ES14A$chl ~ ES14A$Treatment + ES14A$Host + ES14A$isoRepNumber +
## ES14A$techRepNumber + ES14A$LeafSampleNumber)
##
## Coefficients:
## (Intercept) ES14A$TreatmentDMCC2165
## 204.803 -39.317
## ES14A$HostPeanut ES14A$HostSoybean
## 71.821 -20.797
## ES14A$HostTomato ES14A$isoRepNumberisoRep2
## 20.597 8.076
## ES14A$isoRepNumberisoRep3 ES14A$techRepNumbertechRep2
## 10.061 -3.623
## ES14A$techRepNumbertechRep3 ES14A$LeafSampleNumbersample2
## -2.447 -2.221
## ES14A$LeafSampleNumbersample3
## -17.082
summary(ES14A.chl.anova)
##
## Call:
## lm(formula = ES14A$chl ~ ES14A$Treatment + ES14A$Host + ES14A$isoRepNumber +
## ES14A$techRepNumber + ES14A$LeafSampleNumber)
##
## Residuals:
## Min 1Q Median 3Q Max
## -152.26 -25.67 3.28 28.37 140.22
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 204.803 11.492 17.821 < 2e-16 ***
## ES14A$TreatmentDMCC2165 -39.317 6.956 -5.652 5.34e-08 ***
## ES14A$HostPeanut 71.821 9.760 7.359 4.58e-12 ***
## ES14A$HostSoybean -20.797 9.760 -2.131 0.0343 *
## ES14A$HostTomato 20.597 9.914 2.078 0.0390 *
## ES14A$isoRepNumberisoRep2 8.076 8.552 0.944 0.3461
## ES14A$isoRepNumberisoRep3 10.061 8.452 1.190 0.2353
## ES14A$techRepNumbertechRep2 -3.623 8.552 -0.424 0.6723
## ES14A$techRepNumbertechRep3 -2.447 8.552 -0.286 0.7751
## ES14A$LeafSampleNumbersample2 -2.221 8.512 -0.261 0.7944
## ES14A$LeafSampleNumbersample3 -17.082 8.512 -2.007 0.0461 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 50.71 on 202 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.4051, Adjusted R-squared: 0.3756
## F-statistic: 13.75 on 10 and 202 DF, p-value: < 2.2e-16
anova(ES14A.chl.anova)
## Analysis of Variance Table
##
## Response: ES14A$chl
## Df Sum Sq Mean Sq F value Pr(>F)
## ES14A$Treatment 1 81494 81494 31.6869 6.003e-08 ***
## ES14A$Host 3 255475 85158 33.1116 < 2.2e-16 ***
## ES14A$isoRepNumber 2 4050 2025 0.7874 0.4564
## ES14A$techRepNumber 2 478 239 0.0930 0.9112
## ES14A$LeafSampleNumber 2 12250 6125 2.3815 0.0950 .
## Residuals 202 519515 2572
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Tukey's HSD for Variable chl by Treatment
ES14A.chl.treatment.HSD.test <- HSD.test(ES14A.chl.anova, 'ES14A$Treatment', group = T)
ES14A.chl.treatment.HSD.test
## $statistics
## MSerror Df Mean CV
## 2571.854 202 200.2661 25.32304
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES14A$Treatment 2 2.788514 0.05
##
## $means
## ES14A$chl std r Min Max Q25 Q50 Q75
## Control 220.1037 48.30845 105 74.284 312.775 199.7190 220.323 244.5180
## DMCC2165 180.9794 71.63395 108 43.371 317.520 136.5077 190.138 227.8515
##
## $comparison
## NULL
##
## $groups
## ES14A$chl groups
## Control 220.1037 a
## DMCC2165 180.9794 b
##
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable chl by Plant Species
ES14A.chl.host.HSD.test <- HSD.test(ES14A.chl.anova, 'ES14A$Host', group = T)
ES14A.chl.host.HSD.test
## $statistics
## MSerror Df Mean CV
## 2571.854 202 200.2661 25.32304
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES14A$Host 4 3.663584 0.05
##
## $means
## ES14A$chl std r Min Max Q25 Q50 Q75
## Cotton 182.7328 41.22083 54 99.321 258.986 151.8048 189.3455 208.6360
## Peanut 254.5536 39.15515 54 104.832 317.520 232.0955 254.8250 282.4742
## Soybean 161.9354 88.07831 54 43.371 312.775 66.5095 174.5450 226.9425
## Tomato 201.9352 26.66869 51 117.923 244.624 187.7870 203.6790 219.5155
##
## $comparison
## NULL
##
## $groups
## ES14A$chl groups
## Peanut 254.5536 a
## Tomato 201.9352 b
## Cotton 182.7328 bc
## Soybean 161.9354 c
##
## attr(,"class")
## [1] "group"
#Complete ANOVA for ES14A
ES14A.comp.HSD.group <- HSD.test(ES14A.chl.anova, c("ES14A$Treatment", "ES14A$Host"), group=TRUE,console=TRUE)
##
## Study: ES14A.chl.anova ~ c("ES14A$Treatment", "ES14A$Host")
##
## HSD Test for ES14A$chl
##
## Mean Square Error: 2571.854
##
## ES14A$Treatment:ES14A$Host, means
##
## ES14A.chl std r Min Max
## Control:Cotton 194.11622 42.12477 27 106.098 254.411
## Control:Peanut 243.47885 43.34219 27 104.832 305.065
## Control:Soybean 226.62589 63.78820 27 74.284 312.775
## Control:Tomato 215.70517 17.85696 24 183.593 244.624
## DMCC2165:Cotton 171.34937 37.68338 27 99.321 258.986
## DMCC2165:Peanut 265.62833 31.49505 27 200.016 317.520
## DMCC2165:Soybean 97.24481 55.25735 27 43.371 210.220
## DMCC2165:Tomato 189.69526 27.47809 27 117.923 236.489
##
## Alpha: 0.05 ; DF Error: 202
## Critical Value of Studentized Range: 4.331714
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## ES14A$chl groups
## DMCC2165:Peanut 265.62833 a
## Control:Peanut 243.47885 ab
## Control:Soybean 226.62589 abc
## Control:Tomato 215.70517 bc
## Control:Cotton 194.11622 cd
## DMCC2165:Tomato 189.69526 cd
## DMCC2165:Cotton 171.34937 d
## DMCC2165:Soybean 97.24481 e
ES14A.comp.HSD.group
## $statistics
## MSerror Df Mean CV
## 2571.854 202 200.2661 25.32304
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES14A$Treatment:ES14A$Host 8 4.331714 0.05
##
## $means
## ES14A$chl std r Min Max Q25 Q50
## Control:Cotton 194.11622 42.12477 27 106.098 254.411 172.7065 201.2180
## Control:Peanut 243.47885 43.34219 27 104.832 305.065 220.0160 244.4330
## Control:Soybean 226.62589 63.78820 27 74.284 312.775 205.7205 227.9410
## Control:Tomato 215.70517 17.85696 24 183.593 244.624 203.4402 214.3875
## DMCC2165:Cotton 171.34937 37.68338 27 99.321 258.986 146.1180 180.5490
## DMCC2165:Peanut 265.62833 31.49505 27 200.016 317.520 247.1435 262.9750
## DMCC2165:Soybean 97.24481 55.25735 27 43.371 210.220 52.2970 66.4980
## DMCC2165:Tomato 189.69526 27.47809 27 117.923 236.489 178.0980 191.1460
## Q75
## Control:Cotton 229.7960
## Control:Peanut 274.2060
## Control:Soybean 274.5295
## Control:Tomato 227.4280
## DMCC2165:Cotton 198.6270
## DMCC2165:Peanut 290.1215
## DMCC2165:Soybean 143.0605
## DMCC2165:Tomato 206.2940
##
## $comparison
## NULL
##
## $groups
## ES14A$chl groups
## DMCC2165:Peanut 265.62833 a
## Control:Peanut 243.47885 ab
## Control:Soybean 226.62589 abc
## Control:Tomato 215.70517 bc
## Control:Cotton 194.11622 cd
## DMCC2165:Tomato 189.69526 cd
## DMCC2165:Cotton 171.34937 d
## DMCC2165:Soybean 97.24481 e
##
## attr(,"class")
## [1] "group"
#####ES14A.mod.mod###
ES14A.mod.chl.anova <- lm (ES14A.mod$ES14A_chl.tuk ~ ES14A.mod$Treatment +
ES14A.mod$Host +
ES14A.mod$isoRepNumber +
ES14A.mod$techRepNumber +
ES14A.mod$LeafSampleNumber)
ES14A.mod.chl.anova
##
## Call:
## lm(formula = ES14A.mod$ES14A_chl.tuk ~ ES14A.mod$Treatment +
## ES14A.mod$Host + ES14A.mod$isoRepNumber + ES14A.mod$techRepNumber +
## ES14A.mod$LeafSampleNumber)
##
## Coefficients:
## (Intercept) ES14A.mod$TreatmentDMCC2165
## 9573.32 -2709.06
## ES14A.mod$HostPeanut ES14A.mod$HostSoybean
## 6109.14 -562.35
## ES14A.mod$HostTomato ES14A.mod$isoRepNumberisoRep2
## 1457.89 752.80
## ES14A.mod$isoRepNumberisoRep3 ES14A.mod$techRepNumbertechRep2
## 707.59 -175.15
## ES14A.mod$techRepNumbertechRep3 ES14A.mod$LeafSampleNumbersample2
## -380.75 -57.52
## ES14A.mod$LeafSampleNumbersample3
## -831.24
summary(ES14A.mod.chl.anova)
##
## Call:
## lm(formula = ES14A.mod$ES14A_chl.tuk ~ ES14A.mod$Treatment +
## ES14A.mod$Host + ES14A.mod$isoRepNumber + ES14A.mod$techRepNumber +
## ES14A.mod$LeafSampleNumber)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11413 -2124 40 2186 11598
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9573.32 861.86 11.108 < 2e-16 ***
## ES14A.mod$TreatmentDMCC2165 -2709.06 521.66 -5.193 5.03e-07 ***
## ES14A.mod$HostPeanut 6109.14 731.96 8.346 1.10e-14 ***
## ES14A.mod$HostSoybean -562.35 731.96 -0.768 0.4432
## ES14A.mod$HostTomato 1457.89 743.48 1.961 0.0513 .
## ES14A.mod$isoRepNumberisoRep2 752.80 641.39 1.174 0.2419
## ES14A.mod$isoRepNumberisoRep3 707.59 633.89 1.116 0.2656
## ES14A.mod$techRepNumbertechRep2 -175.15 641.39 -0.273 0.7851
## ES14A.mod$techRepNumbertechRep3 -380.75 641.39 -0.594 0.5534
## ES14A.mod$LeafSampleNumbersample2 -57.52 638.34 -0.090 0.9283
## ES14A.mod$LeafSampleNumbersample3 -831.24 638.34 -1.302 0.1943
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3803 on 202 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.3985, Adjusted R-squared: 0.3687
## F-statistic: 13.38 on 10 and 202 DF, p-value: < 2.2e-16
anova(ES14A.mod.chl.anova)
## Analysis of Variance Table
##
## Response: ES14A.mod$ES14A_chl.tuk
## Df Sum Sq Mean Sq F value Pr(>F)
## ES14A.mod$Treatment 1 389423237 389423237 26.9209 5.141e-07 ***
## ES14A.mod$Host 3 1485413072 495137691 34.2289 < 2.2e-16 ***
## ES14A.mod$isoRepNumber 2 25123911 12561955 0.8684 0.4212
## ES14A.mod$techRepNumber 2 5115841 2557921 0.1768 0.8381
## ES14A.mod$LeafSampleNumber 2 30598645 15299322 1.0576 0.3492
## Residuals 202 2922025050 14465471
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Tukey's HSD for Variable chl by Treatment
ES14A.mod.chl.treatment.HSD.test <- HSD.test(ES14A.mod.chl.anova, 'ES14A.mod$Treatment', group = T)
ES14A.mod.chl.treatment.HSD.test
## $statistics
## MSerror Df Mean CV
## 14465471 202 9953.906 38.20962
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES14A.mod$Treatment 2 2.788514 0.05
##
## $means
## ES14A.mod$ES14A_chl.tuk std r Min Max Q25
## Control 11325.224 3958.923 105 1687.6965 20150.01 9294.444
## DMCC2165 8620.679 5150.335 108 667.0663 20680.22 4821.441
## Q50 Q75
## Control 11009.769 13177.60
## DMCC2165 8538.763 11666.87
##
## $comparison
## NULL
##
## $groups
## ES14A.mod$ES14A_chl.tuk groups
## Control 11325.224 a
## DMCC2165 8620.679 b
##
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable chl by Plant Species
ES14A.mod.chl.host.HSD.test <- HSD.test(ES14A.mod.chl.anova, 'ES14A.mod$Host', group = T)
ES14A.mod.chl.host.HSD.test
## $statistics
## MSerror Df Mean CV
## 14465471 202 9953.906 38.20962
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES14A.mod$Host 4 3.663584 0.05
##
## $means
## ES14A.mod$ES14A_chl.tuk std r Min Max Q25
## Cotton 8224.039 3055.399 54 2785.4532 14551.29 5790.517
## Peanut 14333.182 3553.092 54 3057.3977 20680.22 12044.169
## Soybean 7661.688 6130.187 54 667.0663 20150.01 1394.679
## Tomato 9575.703 2068.306 51 3745.4797 13187.45 8357.435
## Q50 Q75
## Cotton 8477.450 10021.99
## Peanut 14150.559 16902.50
## Soybean 7366.996 11586.99
## Tomato 9614.624 10940.26
##
## $comparison
## NULL
##
## $groups
## ES14A.mod$ES14A_chl.tuk groups
## Peanut 14333.182 a
## Tomato 9575.703 b
## Cotton 8224.039 b
## Soybean 7661.688 b
##
## attr(,"class")
## [1] "group"
#Complete ANOVA for ES14A.mod
ES14A.mod.comp.HSD.group <- HSD.test(ES14A.mod.chl.anova, c("ES14A.mod$Treatment",
"ES14A.mod$Host"),
group=TRUE,console=TRUE)
##
## Study: ES14A.mod.chl.anova ~ c("ES14A.mod$Treatment", "ES14A.mod$Host")
##
## HSD Test for ES14A.mod$ES14A_chl.tuk
##
## Mean Square Error: 14465471
##
## ES14A.mod$Treatment:ES14A.mod$Host, means
##
## ES14A.mod.ES14A_chl.tuk std r Min Max
## Control:Cotton 9103.740 3172.165 27 3121.3676 14110.73
## Control:Peanut 13336.356 3754.679 27 3057.3977 19300.87
## Control:Soybean 12128.331 5109.049 27 1687.6965 20150.01
## Control:Tomato 10658.376 1512.759 24 8038.0704 13187.45
## DMCC2165:Cotton 7344.338 2712.946 27 2785.4532 14551.29
## DMCC2165:Peanut 15330.007 3094.045 27 9318.2997 20680.22
## DMCC2165:Soybean 3195.045 3010.793 27 667.0663 10153.43
## DMCC2165:Tomato 8613.327 2039.245 27 3745.4797 12440.10
##
## Alpha: 0.05 ; DF Error: 202
## Critical Value of Studentized Range: 4.331714
##
## Groups according to probability of means differences and alpha level( 0.05 )
##
## Treatments with the same letter are not significantly different.
##
## ES14A.mod$ES14A_chl.tuk groups
## DMCC2165:Peanut 15330.007 a
## Control:Peanut 13336.356 ab
## Control:Soybean 12128.331 bc
## Control:Tomato 10658.376 bcd
## Control:Cotton 9103.740 cde
## DMCC2165:Tomato 8613.327 de
## DMCC2165:Cotton 7344.338 e
## DMCC2165:Soybean 3195.045 f
ES14A.mod.comp.HSD.group
## $statistics
## MSerror Df Mean CV
## 14465471 202 9953.906 38.20962
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES14A.mod$Treatment:ES14A.mod$Host 8 4.331714 0.05
##
## $means
## ES14A.mod$ES14A_chl.tuk std r Min Max
## Control:Cotton 9103.740 3172.165 27 3121.3676 14110.73
## Control:Peanut 13336.356 3754.679 27 3057.3977 19300.87
## Control:Soybean 12128.331 5109.049 27 1687.6965 20150.01
## Control:Tomato 10658.376 1512.759 24 8038.0704 13187.45
## DMCC2165:Cotton 7344.338 2712.946 27 2785.4532 14551.29
## DMCC2165:Peanut 15330.007 3094.045 27 9318.2997 20680.22
## DMCC2165:Soybean 3195.045 3010.793 27 667.0663 10153.43
## DMCC2165:Tomato 8613.327 2039.245 27 3745.4797 12440.10
## Q25 Q50 Q75
## Control:Cotton 7235.4241 9415.107 11840.543
## Control:Peanut 10983.3242 13169.695 16057.689
## Control:Soybean 9787.4152 11674.646 16090.596
## Control:Tomato 9595.2157 10503.255 11629.391
## DMCC2165:Cotton 5421.6757 7809.559 9207.086
## DMCC2165:Peanut 13422.7747 14940.064 17699.067
## DMCC2165:Soybean 921.2485 1394.263 5240.336
## DMCC2165:Tomato 7629.0246 8616.978 9828.773
##
## $comparison
## NULL
##
## $groups
## ES14A.mod$ES14A_chl.tuk groups
## DMCC2165:Peanut 15330.007 a
## Control:Peanut 13336.356 ab
## Control:Soybean 12128.331 bc
## Control:Tomato 10658.376 bcd
## Control:Cotton 9103.740 cde
## DMCC2165:Tomato 8613.327 de
## DMCC2165:Cotton 7344.338 e
## DMCC2165:Soybean 3195.045 f
##
## attr(,"class")
## [1] "group"
Extract the information needed for panel “A”
##Extract all control (ES5: 7 DOE)
ES5.control <- subset(ES5.mod, Treatment== "control")
ES5.Xn <- subset(ES5.mod, Treatment== c("DMCC2126", "DMCC2127", "DMCC2165"))
ES5.control <- ES5.control %>%
add_column(Species = "control")
ES5.Xn <- ES5.Xn %>%
add_column(Species = "X.necrophora")
ES5.mod.v2 <- rbind(ES5.control, ES5.Xn)
ES5.mod.ggplot <- ggplot(ES5.mod.v2, aes(x = reorder(Species, -chl, na.rm = TRUE),
y = chl, fill = Species)) +
geom_boxplot() + #geom_point(aes(colour = factor(LeafSampleNumber)))# + geom_jitter()
#scale_fill_grey(start = 1, end = 0.4) + labs(tag = "A") +
scale_fill_manual(values = c("#FFFFFF", "#545454"))+ labs(tag = "A") +
xlab("Treatment") + ylab("Total Chlorophyll (ng/sq mm)") +
theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
facet_wrap(~ Dilution)
ES5.mod.ggplot
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
##Extract all control (ES2), colletrichum, and X. necrophora
ES2.control <- subset(ES2.mod, Treatment== "control")
ES2.coll <- subset(ES2.mod, Treatment== "DMCC2966")
ES2.Xn <- subset(ES2.mod, Treatment== c("DMCC2126", "DMCC2127", "DMCC2165"))
ES2.control <- ES2.control %>%
add_column(Species = "control")
ES2.coll <- ES2.coll %>%
add_column(Species = "C.siamense")
ES2.Xn <- ES2.Xn %>%
add_column(Species = "X.necrophora")
ES2.mod.v2 <- rbind(ES2.control, ES2.coll, ES2.Xn)
#plot for figure by species by dilution factor
#Reorganizing for plotting
ES2.mod.v2$Species <- factor(ES2.mod.v2$Species,
levels = c("control", "C.siamense", "X.necrophora"))
ES2.mod.v2.ggplot <- ggplot(ES2.mod.v2, aes(x = reorder(Species, -chl, na.rm = TRUE),
y = chl, fill = Species)) + geom_boxplot() + #geom_point(aes(colour = factor(LeafSampleNumber)))# + geom_jitter()
# scale_fill_grey("control" = 1, "C.siamense" =0.7, "X.necrophora"= 0.4)
scale_fill_manual(values = c("#FFFFFF", "#AAAAAA", "#545454"))+ labs(tag = "B") +
xlab("Treatment") + ylab("Total Chlorophyll (ng/sq mm)") +
theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
facet_wrap(~ Dilution)
ES2.mod.v2.ggplot
## Warning: Removed 32 rows containing non-finite values (stat_boxplot).
###Grid for composite figure 1 (updated 10/25/2021). Using ES2 and ES5 only.
gridExtra::grid.arrange(ES5.mod.ggplot, ES2.mod.v2.ggplot, ncol=2)
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Warning: Removed 32 rows containing non-finite values (stat_boxplot).
###Plot HostVariety only w/ outliers
ES13B.ByHosCult <- ggplot(ES13B.mod, aes(x = reorder(HostVariety, -chl, na.rm = TRUE),
y = chl, fill=HostVariety)) +
geom_boxplot() +
scale_fill_grey(start = 1, end = 0.4) + labs(tag = "A") +
xlab("Soybean Cultivar") + ylab("Total Chlorophyll (ng/sq mm)") +
theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"))
ES13B.ByHosCult
## Warning: Removed 6 rows containing non-finite values (stat_boxplot).
###Plot by variety by treatment w/ outliers
ES13B.ggplot.ByCultByTreat <- ggplot(ES13B.mod, aes(x = reorder(HostVariety, -chl,
na.rm = TRUE),
y = chl, fill=Treatment)) +
geom_boxplot() + #+ geom_point(aes(colour = factor(LeafSampleNumber)))# + geom_jitter()
scale_fill_grey(start = 1, end = 0.4) + labs(tag = "B") +
xlab("Soybean Cultivar") + ylab("Total Chlorophyll (ng/sq mm)") +
theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y =element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"))
ES13B.ggplot.ByCultByTreat
## Warning: Removed 6 rows containing non-finite values (stat_boxplot).
###Plot By Host only w/ outliers for grid
ES14A.ggplot.ByHost <- ggplot(ES14A.mod, aes(x = reorder(Host, -chl, na.rm = TRUE),
y = chl, fill=Host)) +
geom_boxplot() + #+ geom_point(aes(colour = factor(LeafSampleNumber)))# + geom_jitter()
scale_fill_grey(start = 1, end = 0.4) + labs(tag = "C") +
xlab("Plant Species") + ylab("Total Chlorophyll (ng/sq mm)") +
theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"))
ES14A.ggplot.ByHost
## Warning: Removed 3 rows containing non-finite values (stat_boxplot).
###Plot by host by treatment w/ outliers
ES14A.ggplot.ByHostByTreat <- ggplot(ES14A.mod, aes(x = reorder(Host, -chl, na.rm = TRUE),
y = chl, fill=Treatment)) +
geom_boxplot() + #+ geom_point(aes(colour = factor(LeafSampleNumber)))# + geom_jitter()
scale_fill_grey(start = 1, end = 0.4) + labs(tag = "D") +
xlab("Plant Species") + ylab("Total Chlorophyll (ng/sq mm)") +
theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"))
ES14A.ggplot.ByHostByTreat
## Warning: Removed 3 rows containing non-finite values (stat_boxplot).
###Grid for composite figure 3 (08/16/2021). Using ES13B and ES14 only.
gridExtra::grid.arrange(ES13B.ByHosCult,
ES13B.ggplot.ByCultByTreat ,
ES14A.ggplot.ByHost,
ES14A.ggplot.ByHostByTreat, ncol=2)
## Warning: Removed 6 rows containing non-finite values (stat_boxplot).
## Warning: Removed 6 rows containing non-finite values (stat_boxplot).
## Warning: Removed 3 rows containing non-finite values (stat_boxplot).
## Warning: Removed 3 rows containing non-finite values (stat_boxplot).
#ES2 by treatment by dilution, by growth conditions no title
ES2.mod.ggplot.v2 <- ggplot(ES2.mod, aes(x = reorder(Treatment, -chl, na.rm = TRUE),
y = chl, fill = Dilution)) +
geom_boxplot() + #geom_point(aes(colour = factor(LeafSampleNumber)))# + geom_jitter()
scale_fill_grey(start = 1, end = 0.4) + labs(tag = "A") +
xlab("Treatment") + ylab("Total Chlorophyll (ng/sq mm)") +
theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"))
ES2.mod.ggplot.v2
## Warning: Removed 60 rows containing non-finite values (stat_boxplot).
#ES5 by treatment by dilution, no title
ES5.mod.ggplot.v2 <- ggplot(ES5.mod, aes(x = reorder(Treatment, -chl, na.rm = TRUE),
y = chl, fill = Dilution)) +
geom_boxplot() + #geom_point(aes(colour = factor(LeafSampleNumber)))# + geom_jitter()
scale_fill_grey(start = 1, end = 0.4) + labs(tag = "B") +
xlab("Treatment") + ylab("Total Chlorophyll (ng/sq mm)") +
theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
facet_wrap(~ Condition)
ES5.mod.ggplot.v2
## Warning: Removed 12 rows containing non-finite values (stat_boxplot).
#ES5 by conditions (side by side)
ES5.mod.ggplot.v3 <- ggplot(ES5.mod, aes(x = reorder(Condition, -chl, na.rm = TRUE),
y = chl, fill=Treatment)) +
geom_boxplot() +
scale_fill_grey(start =1, end = 0.4) + labs(tag = "C") +
xlab("Growth Condition") + ylab("Total Chlorophyll (ng/sq mm)") +
theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"))
ES5.mod.ggplot.v3
## Warning: Removed 12 rows containing non-finite values (stat_boxplot).
#ES5 by dilutions (side by side)
ES5.mod.ggplot.v4 <- ggplot(ES5.mod, aes(x = reorder(Dilution, -chl, na.rm = TRUE),
y = chl, fill=Treatment)) +
geom_boxplot() +
scale_fill_grey(start =1, end = 0.4) + labs(tag = "D") +
xlab("Dilution Factor") + ylab("Total Chlorophyll (ng/sq mm)") +
theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"))
ES5.mod.ggplot.v4
## Warning: Removed 12 rows containing non-finite values (stat_boxplot).
###Grid for supplementary figure 1 (updated 08/25/2021). Using ES2 and ES5 only.
gridExtra::grid.arrange(ES2.mod.ggplot.v2,
ES5.mod.ggplot.v2,
ES5.mod.ggplot.v3,ES5.mod.ggplot.v4, ncol=2)
## Warning: Removed 60 rows containing non-finite values (stat_boxplot).
## Warning: Removed 12 rows containing non-finite values (stat_boxplot).
## Warning: Removed 12 rows containing non-finite values (stat_boxplot).
## Warning: Removed 12 rows containing non-finite values (stat_boxplot).
This composite figure contained validation chlorophyll content (chemical vs digital extractions) on panel A, fungal biomass based on Whatmat No 1 filter weight on panel B, measurements of pH from initial potato dextrose broth and filtered stock cell-free culture filtrates (CFCFs) on panel C, and the pearson correlation between final pH and digital chlorophyll content on panel D.
#Load datasets
ES10.chem <- read.csv("../raw_data/ES10.chem.chl.csv", header = T)
#Chlorophyll content obtained chemically for a dataset with all biomass and pH measurements
ES10.digital <- read.csv("../raw_data/ES10.digital.chl.csv", header = T)
#Chlorophyll content obtained digitally for a dataset with all biomass and pH measurements
BiomassAndpH.metadata <- read.csv("../raw_data/FilteringTreatments.metadata.csv",
header = T)
#Obtaining sums for ES10 because digital measurements=3 per experimental unit,
#but chemical measurements=1 per experimental unit.
ES10.digital.sum <- aggregate(ES10.digital$chl,list(ES10.digital$ExpCode),sum)
names(ES10.digital.sum)[names(ES10.digital.sum) == "x"] <- "dig.chl"
#Merging ES10 chem and ES10 digital
ES10.chem.dig = merge(ES10.chem, ES10.digital.sum, by.x='ExpCode', by.y='Group.1')
#Pearson correlations for ES10
cor(ES10.chem.dig$chl, ES10.chem.dig$dig.chl, method="pearson")
## [1] 0.8450695
ES10.chem.dig.ggplot <- ggscatter(ES10.chem.dig, x = "chl", y = "dig.chl",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE,
cor.method = "pearson",
xlab = "Chemical chlorophyll content (ng/sq mm)",
ylab = "Digital chlorophyll content (ng/sq mm)") +
labs(tag = "A")
ES10.chem.dig.ggplot
## `geom_smooth()` using formula 'y ~ x'
(Supplementary Figure 2, Panel B)
# Supplementary figure 2 panel B
## ES5 by dilutions (side by side)
BiomassAndpH.metadata.ggplot.B <- ggplot(BiomassAndpH.metadata,
aes(x = reorder(Isolate, +Weight_grams),
y = Weight_grams, fill=FilterWeight)) +
geom_boxplot() + #geom_point(aes(colour = factor(LeafSampleNumber)))# + geom_jitter()
scale_fill_grey(start =0.4, end = 1) + labs(tag = "B") +
xlab("Treatment") + ylab("Whatman No. 1 Filter Weight (grams)") +
theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
facet_wrap(~ Condition)
BiomassAndpH.metadata.ggplot.B
#ES5 by dilutions (side by side)
BiomassAndpH.metadata.pH.ggplot.C <- ggplot(BiomassAndpH.metadata,
aes(x = reorder(Isolate, +pH),
y = pH, fill=pHMeasurement)) +
geom_boxplot() +
scale_fill_grey(start =0.4, end = 1) + labs(tag = "C") +
xlab("Treatment") + ylab("pH") +
theme(plot.title = element_text(size = 12, hjust = 0.1, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
facet_wrap(~ Condition)
BiomassAndpH.metadata.pH.ggplot.C
ES8.chem <- read.csv("../raw_data/ES8_chem.chl.csv", header = T)
ES8.digital <- read.csv("../raw_data/ES8.digital.chl.csv", header = T)
ES8.digital.sum <-aggregate(ES8.digital$chl,list(ES8.digital$ExpCode),sum)
names(ES8.digital.sum)[names(ES8.digital.sum) == "x"] <- "dig.chl"
ES8.chem.dig = merge(ES8.chem, ES8.digital.sum, by.x='ExpCode', by.y='Group.1')
FinalpHvsChl.reg <- ggscatter(ES8.chem.dig, x = "dig.chl", y = "FinalpH",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Digital chlorophyll content (ng/sq mm)",
ylab = "Final pH") + labs(tag = "D")
FinalpHvsChl.reg
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 11 rows containing non-finite values (stat_smooth).
## Warning: Removed 11 rows containing non-finite values (stat_cor).
## Warning: Removed 11 rows containing missing values (geom_point).
gridExtra::grid.arrange(ES10.chem.dig.ggplot, BiomassAndpH.metadata.ggplot.B, BiomassAndpH.metadata.pH.ggplot.C, FinalpHvsChl.reg, ncol=2)
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 11 rows containing non-finite values (stat_smooth).
## Warning: Removed 11 rows containing non-finite values (stat_cor).
## Warning: Removed 11 rows containing missing values (geom_point).
ES2.root <- read.csv("../raw_data/ES2.rootMeasurements.csv", header = T)
#Clean dataset for plotting and analyses
ES2.root.noNAs <- na.omit(ES2.root)
#ES2 longest root statistical analysis
ES2.root.noNAs.lm <- lm (ES2.root.noNAs$Length ~ ES2.root.noNAs$Isolate + ES2.root.noNAs$Condition + ES2.root.noNAs$Concentration, na.action=na.exclude)
ES2.root.noNAs.lm
##
## Call:
## lm(formula = ES2.root.noNAs$Length ~ ES2.root.noNAs$Isolate +
## ES2.root.noNAs$Condition + ES2.root.noNAs$Concentration,
## na.action = na.exclude)
##
## Coefficients:
## (Intercept) ES2.root.noNAs$IsolateDMCC2126
## 38.608 -10.916
## ES2.root.noNAs$IsolateDMCC2127 ES2.root.noNAs$IsolateDMCC2165
## -8.786 -12.099
## ES2.root.noNAs$IsolateDMCC2966 ES2.root.noNAs$ConditionStationary
## 13.649 -6.885
## ES2.root.noNAs$Concentration25fold
## -25.132
summary(ES2.root.noNAs.lm)
##
## Call:
## lm(formula = ES2.root.noNAs$Length ~ ES2.root.noNAs$Isolate +
## ES2.root.noNAs$Condition + ES2.root.noNAs$Concentration,
## na.action = na.exclude)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.264 -8.173 1.284 7.818 22.674
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 38.608 3.659 10.550 3.33e-15 ***
## ES2.root.noNAs$IsolateDMCC2126 -10.916 5.457 -2.000 0.05008 .
## ES2.root.noNAs$IsolateDMCC2127 -8.786 5.223 -1.682 0.09781 .
## ES2.root.noNAs$IsolateDMCC2165 -12.099 4.986 -2.427 0.01832 *
## ES2.root.noNAs$IsolateDMCC2966 13.649 4.199 3.250 0.00191 **
## ES2.root.noNAs$ConditionStationary -6.885 3.178 -2.167 0.03431 *
## ES2.root.noNAs$Concentration25fold -25.132 3.492 -7.197 1.26e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.49 on 59 degrees of freedom
## Multiple R-squared: 0.5919, Adjusted R-squared: 0.5504
## F-statistic: 14.26 on 6 and 59 DF, p-value: 5.795e-10
anova(ES2.root.noNAs.lm)
## Analysis of Variance Table
##
## Response: ES2.root.noNAs$Length
## Df Sum Sq Mean Sq F value Pr(>F)
## ES2.root.noNAs$Isolate 4 4955.4 1238.8 7.9369 3.450e-05 ***
## ES2.root.noNAs$Condition 1 317.6 317.6 2.0349 0.159
## ES2.root.noNAs$Concentration 1 8084.1 8084.1 51.7926 1.256e-09 ***
## Residuals 59 9209.1 156.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Tukey's HSD for Variable Condition
ES2.root.noNAs.condition.HSD.test <- HSD.test(ES2.root.noNAs.lm,
'ES2.root.noNAs$Condition', group = T)
ES2.root.noNAs.condition.HSD.test
## $statistics
## MSerror Df Mean CV
## 156.086 59 26.46406 47.20907
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES2.root.noNAs$Condition 2 2.829835 0.05
##
## $means
## ES2.root.noNAs$Length std r Min Max Q25 Q50 Q75
## Shaking 27.54116 19.14552 37 0.759 67.578 14.983 24.544 36.420
## Stationary 25.08983 18.19797 29 0.982 68.045 13.602 17.404 38.714
##
## $comparison
## NULL
##
## $groups
## ES2.root.noNAs$Length groups
## Shaking 27.54116 a
## Stationary 25.08983 a
##
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable Concentration
ES2.root.noNAs.Concentration.HSD.test <- HSD.test(ES2.root.noNAs.lm, 'ES2.root.noNAs$Concentration', group = T)
ES2.root.noNAs.Concentration.HSD.test
## $statistics
## MSerror Df Mean CV
## 156.086 59 26.46406 47.20907
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES2.root.noNAs$Concentration 2 2.829835 0.05
##
## $means
## ES2.root.noNAs$Length std r Min Max Q25 Q50 Q75
## 100fold 33.41979 18.02719 43 2.261 68.045 16.7635 31.069 47.0615
## 25fold 13.45987 11.57407 23 0.759 38.442 1.8595 14.252 19.0160
##
## $comparison
## NULL
##
## $groups
## ES2.root.noNAs$Length groups
## 100fold 33.41979 a
## 25fold 13.45987 b
##
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable Isolate
ES2.root.noNAs.isolate.HSD.test <- HSD.test(ES2.root.noNAs.lm, 'ES2.root.noNAs$Isolate', group = T)
ES2.root.noNAs.isolate.HSD.test
## $statistics
## MSerror Df Mean CV
## 156.086 59 26.46406 47.20907
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES2.root.noNAs$Isolate 5 3.97949 0.05
##
## $means
## ES2.root.noNAs$Length std r Min Max Q25 Q50 Q75
## Control 25.46106 14.42338 16 0.759 53.277 15.1875 21.304 32.42575
## DMCC2126 23.86656 15.08114 9 2.261 43.013 13.8810 28.594 35.49300
## DMCC2127 13.56456 13.67932 9 1.131 36.420 1.7050 15.283 15.82100
## DMCC2165 18.80955 13.95768 11 0.885 46.821 10.3030 15.075 27.10500
## DMCC2966 37.87933 21.47743 21 0.982 68.045 24.5440 33.212 58.57400
##
## $comparison
## NULL
##
## $groups
## ES2.root.noNAs$Length groups
## DMCC2966 37.87933 a
## Control 25.46106 b
## DMCC2126 23.86656 b
## DMCC2165 18.80955 b
## DMCC2127 13.56456 b
##
## attr(,"class")
## [1] "group"
#Tukey's HSD for Treatment and concentration
ES2.root.noNAs.leafsec.treat.dil.HSD.test <- HSD.test(ES2.root.noNAs.lm, c('ES2.root.noNAs$Isolate', 'ES2.root.noNAs$Concentration'), group = T )
ES2.root.noNAs.leafsec.treat.dil.HSD.test
## $statistics
## MSerror Df Mean CV
## 156.086 59 26.46406 47.20907
##
## $parameters
## test name.t ntr
## Tukey ES2.root.noNAs$Isolate:ES2.root.noNAs$Concentration 9
## StudentizedRange alpha
## 4.55324 0.05
##
## $means
## ES2.root.noNAs$Length std r Min Max Q25
## Control:100fold 34.51244 12.257238 9 19.375 53.277 26.42600
## Control:25fold 13.82357 6.234620 7 0.759 20.628 13.92700
## DMCC2126:100fold 23.86656 15.081139 9 2.261 43.013 13.88100
## DMCC2127:100fold 25.15625 11.174660 4 15.283 36.420 15.68650
## DMCC2127:25fold 4.29120 6.223480 5 1.131 15.405 1.20100
## DMCC2165:100fold 22.60056 12.426130 9 7.425 46.821 14.98300
## DMCC2165:25fold 1.75000 1.223295 2 0.885 2.615 1.31750
## DMCC2966:100fold 50.63417 17.328417 12 15.108 68.045 43.24375
## DMCC2966:25fold 20.87289 13.073765 9 0.982 38.442 13.88400
## Q50 Q75
## Control:100fold 30.2620 41.43500
## Control:25fold 14.8050 16.35950
## DMCC2126:100fold 28.5940 35.49300
## DMCC2127:100fold 24.4610 33.93075
## DMCC2127:25fold 1.7050 2.01400
## DMCC2165:100fold 17.7060 30.29700
## DMCC2165:25fold 1.7500 2.18250
## DMCC2966:100fold 55.6675 64.10850
## DMCC2966:25fold 24.5440 29.70700
##
## $comparison
## NULL
##
## $groups
## ES2.root.noNAs$Length groups
## DMCC2966:100fold 50.63417 a
## Control:100fold 34.51244 ab
## DMCC2127:100fold 25.15625 bc
## DMCC2126:100fold 23.86656 bc
## DMCC2165:100fold 22.60056 bc
## DMCC2966:25fold 20.87289 bc
## Control:25fold 13.82357 c
## DMCC2127:25fold 4.29120 c
## DMCC2165:25fold 1.75000 c
##
## attr(,"class")
## [1] "group"
# Used the same Tukey's normalization methods used above
ES2.root.tuk = transformTukey(ES2.root.noNAs$Length, plotit=FALSE)
##
## lambda W Shapiro.p.value
## 427 0.65 0.964 0.0525
##
## if (lambda > 0){TRANS = x ^ lambda}
## if (lambda == 0){TRANS = log(x)}
## if (lambda < 0){TRANS = -1 * x ^ lambda}
ES2.root.noNAs.mod = cbind(ES2.root.noNAs, ES2.root.tuk)
#ES2 longest root statistical analysis after normalization
ES2.root.noNAs.mod.lm <- lm (ES2.root.noNAs.mod$ES2.root.tuk ~ ES2.root.noNAs.mod$Isolate +
ES2.root.noNAs.mod$Condition +
ES2.root.noNAs.mod$Concentration, na.action=na.exclude)
ES2.root.noNAs.mod.lm
##
## Call:
## lm(formula = ES2.root.noNAs.mod$ES2.root.tuk ~ ES2.root.noNAs.mod$Isolate +
## ES2.root.noNAs.mod$Condition + ES2.root.noNAs.mod$Concentration,
## na.action = na.exclude)
##
## Coefficients:
## (Intercept) ES2.root.noNAs.mod$IsolateDMCC2126
## 10.769 -2.553
## ES2.root.noNAs.mod$IsolateDMCC2127 ES2.root.noNAs.mod$IsolateDMCC2165
## -2.390 -2.826
## ES2.root.noNAs.mod$IsolateDMCC2966 ES2.root.noNAs.mod$ConditionStationary
## 2.501 -1.414
## ES2.root.noNAs.mod$Concentration25fold
## -5.617
summary(ES2.root.noNAs.mod.lm)
##
## Call:
## lm(formula = ES2.root.noNAs.mod$ES2.root.tuk ~ ES2.root.noNAs.mod$Isolate +
## ES2.root.noNAs.mod$Condition + ES2.root.noNAs.mod$Concentration,
## na.action = na.exclude)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.015 -1.626 0.381 1.994 4.728
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.7694 0.7898 13.635 < 2e-16 ***
## ES2.root.noNAs.mod$IsolateDMCC2126 -2.5526 1.1779 -2.167 0.0343 *
## ES2.root.noNAs.mod$IsolateDMCC2127 -2.3895 1.1273 -2.120 0.0382 *
## ES2.root.noNAs.mod$IsolateDMCC2165 -2.8263 1.0762 -2.626 0.0110 *
## ES2.root.noNAs.mod$IsolateDMCC2966 2.5010 0.9064 2.759 0.0077 **
## ES2.root.noNAs.mod$ConditionStationary -1.4140 0.6859 -2.062 0.0437 *
## ES2.root.noNAs.mod$Concentration25fold -5.6168 0.7537 -7.452 4.64e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.697 on 59 degrees of freedom
## Multiple R-squared: 0.5978, Adjusted R-squared: 0.5569
## F-statistic: 14.61 on 6 and 59 DF, p-value: 3.856e-10
anova(ES2.root.noNAs.mod.lm)
## Analysis of Variance Table
##
## Response: ES2.root.noNAs.mod$ES2.root.tuk
## Df Sum Sq Mean Sq F value Pr(>F)
## ES2.root.noNAs.mod$Isolate 4 221.55 55.39 7.6175 5.116e-05 ***
## ES2.root.noNAs.mod$Condition 1 12.18 12.18 1.6751 0.2006
## ES2.root.noNAs.mod$Concentration 1 403.79 403.79 55.5332 4.637e-10 ***
## Residuals 59 429.00 7.27
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Tukey's HSD for Variable Condition
ES2.root.noNAs.mod.condition.HSD.test <- HSD.test(ES2.root.noNAs.mod.lm, 'ES2.root.noNAs.mod$Condition', group = T)
ES2.root.noNAs.mod.condition.HSD.test
## $statistics
## MSerror Df Mean CV
## 7.271182 59 7.841521 34.38763
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES2.root.noNAs.mod$Condition 2 2.829835 0.05
##
## $means
## ES2.root.noNAs.mod$ES2.root.tuk std r Min Max
## Shaking 8.046515 4.162235 37 0.8359054 15.46584
## Stationary 7.579976 3.961030 29 0.9882628 15.53522
## Q25 Q50 Q75
## Shaking 5.809506 8.006901 10.34835
## Stationary 5.455591 6.403566 10.76748
##
## $comparison
## NULL
##
## $groups
## ES2.root.noNAs.mod$ES2.root.tuk groups
## Shaking 8.046515 a
## Stationary 7.579976 a
##
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable Concentration
ES2.root.noNAs.mod.Concentration.HSD.test <- HSD.test(ES2.root.noNAs.mod.lm, 'ES2.root.noNAs.mod$Concentration', group = T)
ES2.root.noNAs.mod.Concentration.HSD.test
## $statistics
## MSerror Df Mean CV
## 7.271182 59 7.841521 34.38763
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES2.root.noNAs.mod$Concentration 2 2.829835 0.05
##
## $means
## ES2.root.noNAs.mod$ES2.root.tuk std r Min Max Q25
## 100fold 9.428687 3.551457 43 1.6993990 15.53522 6.247133
## 25fold 4.874211 3.204748 23 0.8359054 10.71825 1.495429
## Q50 Q75
## 100fold 9.332817 12.224510
## 25fold 5.623663 6.777533
##
## $comparison
## NULL
##
## $groups
## ES2.root.noNAs.mod$ES2.root.tuk groups
## 100fold 9.428687 a
## 25fold 4.874211 b
##
## attr(,"class")
## [1] "group"
#Tukey's HSD for Variable Isolate
ES2.root.noNAs.mod.isolate.HSD.test <- HSD.test(ES2.root.noNAs.mod.lm, 'ES2.root.noNAs.mod$Isolate', group = T)
ES2.root.noNAs.mod.isolate.HSD.test
## $statistics
## MSerror Df Mean CV
## 7.271182 59 7.841521 34.38763
##
## $parameters
## test name.t ntr StudentizedRange alpha
## Tukey ES2.root.noNAs.mod$Isolate 5 3.97949 0.05
##
## $means
## ES2.root.noNAs.mod$ES2.root.tuk std r Min Max
## Control 7.870193 3.154754 16 0.8359054 13.25107
## DMCC2126 7.431287 3.579335 9 1.6993990 11.53028
## DMCC2127 4.788109 3.676082 9 1.0833049 10.34835
## DMCC2165 6.279095 3.393188 11 0.9236621 12.18390
## DMCC2966 10.122508 4.300847 21 0.9882628 15.53522
## Q25 Q50 Q75
## Control 5.860782 7.302137 9.581929
## DMCC2126 5.528069 8.842574 10.176367
## DMCC2127 1.414558 5.884853 6.018691
## DMCC2165 4.513060 5.832668 8.526954
## DMCC2966 8.006901 9.746345 14.093153
##
## $comparison
## NULL
##
## $groups
## ES2.root.noNAs.mod$ES2.root.tuk groups
## DMCC2966 10.122508 a
## Control 7.870193 ab
## DMCC2126 7.431287 ab
## DMCC2165 6.279095 b
## DMCC2127 4.788109 b
##
## attr(,"class")
## [1] "group"
#Tukey's HSD for Treatment and concentration
ES2.root.noNAs.mod.leafsec.treat.dil.HSD.test <- HSD.test(ES2.root.noNAs.mod.lm, c('ES2.root.noNAs.mod$Isolate', 'ES2.root.noNAs.mod$Concentration'), group = T )
ES2.root.noNAs.mod.leafsec.treat.dil.HSD.test
## $statistics
## MSerror Df Mean CV
## 7.271182 59 7.841521 34.38763
##
## $parameters
## test name.t ntr
## Tukey ES2.root.noNAs.mod$Isolate:ES2.root.noNAs.mod$Concentration 9
## StudentizedRange alpha
## 4.55324 0.05
##
## $means
## ES2.root.noNAs.mod$ES2.root.tuk std r Min
## Control:100fold 9.866162 2.2929937 9 6.8660524
## Control:25fold 5.303948 2.0522425 7 0.8359054
## DMCC2126:100fold 7.431287 3.5793348 9 1.6993990
## DMCC2127:100fold 7.994262 2.3727767 4 5.8848527
## DMCC2127:25fold 2.223187 2.0740415 5 1.0833049
## DMCC2165:100fold 7.364275 2.6551730 9 3.6808888
## DMCC2165:25fold 1.395787 0.6676861 2 0.9236621
## DMCC2966:100fold 12.625081 3.0906845 12 5.8409641
## DMCC2966:25fold 6.785744 3.3449518 9 0.9882628
## Max Q25 Q50 Q75
## Control:100fold 13.251067 8.400796 9.174522 11.253530
## Control:25fold 7.151500 5.539627 5.764551 6.148213
## DMCC2126:100fold 11.530279 5.528069 8.842574 10.176367
## DMCC2127:100fold 10.348346 5.985232 7.871925 9.880956
## DMCC2127:25fold 5.915345 1.126427 1.414558 1.576299
## DMCC2165:100fold 12.183903 5.809506 6.475574 9.181418
## DMCC2165:25fold 1.867913 1.159725 1.395787 1.631850
## DMCC2966:100fold 15.535225 11.532042 13.630329 14.944422
## DMCC2966:25fold 10.718250 5.528846 8.006901 9.064800
##
## $comparison
## NULL
##
## $groups
## ES2.root.noNAs.mod$ES2.root.tuk groups
## DMCC2966:100fold 12.625081 a
## Control:100fold 9.866162 ab
## DMCC2127:100fold 7.994262 abc
## DMCC2126:100fold 7.431287 bc
## DMCC2165:100fold 7.364275 bc
## DMCC2966:25fold 6.785744 bc
## Control:25fold 5.303948 c
## DMCC2127:25fold 2.223187 c
## DMCC2165:25fold 1.395787 c
##
## attr(,"class")
## [1] "group"
#Plate for Supp Figure 3 FINAL (USE THIS ONE, because no differences between Shaking and stat were observed)
ES2.root.noNAs.mod$Isolate <- with(ES2.root.noNAs.mod, reorder(Isolate, -Length))
ES2.root.noNAs.mod.ggplot.plate <- ggplot(ES2.root.noNAs.mod, aes(x = Isolate,
y = Length,
fill = Isolate)) +
geom_boxplot() +
#scale_fill_grey(start = 1, end = 0.4) +
#scale_fill_manual(values = c("Control"="green", "DMCC2966"="green", "DMCC2126"="gold", "DMCC2165"="gold", "DMCC2127"="gold"))+
#ggtitle("Root Length at 14 Days After Exposure") +
scale_fill_manual(values = c("#000000", "#FFFFFF", "#DADADA", "#ACACAC","#666666")) +
xlab("Treatment") + ylab("Root Length (mm)") +
theme(plot.title = element_text(size = 14, hjust = 0.5, face = "bold"),
axis.title.x = element_text(size=10, face = "bold"),
axis.title.y = element_text(size = 10, face = "bold"),
axis.text.x = element_text(angle = 30, hjust = 1)) +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")) +
facet_wrap(~ Concentration)
ES2.root.noNAs.mod.ggplot.plate
#dev.off()